Notebook

In [3]:

# Copyright 2023 Shane Khalid. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================License.

Stock Prediction Deep Learning Model¶

Stock/Asset prices are time-series data and so I am implementing a LSTM (Long Short-Term Memory) which is a type of RNN (Recurrent Neural Network) that can remember information over a long period of time.¶

Import libraries. I normally use yfinance for this, but I have a clean dataset loading in¶

In [1]:

import yfinance as yf

import tensorflow as tf
from tensorflow import keras

from keras.layers import GRU, Dropout, SimpleRNN, LSTM, Dense, SimpleRNN, GRU
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))

import torch
if torch.cuda.is_available():
    device = torch.device("cuda")
    print("GPU is available and being used.")
else:
    device = torch.device("cpu")
    print("GPU is not available, using CPU instead.")

import pandas as pd
import numpy as np

import plotly.express as px
import statsmodels.api as sm
import matplotlib.pyplot as plt
import plotly.graph_objects as go

import matplotlib.pyplot as plt
import matplotlib.dates as dates
import seaborn as sns
import math
import datetime
import keras
import warnings
warnings.filterwarnings('ignore')

from datetime import date, timedelta
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
from keras.layers import *
from keras.callbacks import EarlyStopping

from keras.metrics import Accuracy
from keras.metrics import F1Score
from keras.metrics import Precision


from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
#from sklearn.metrics import accuracy_score
#from sklearn.metrics import precision_score
#from sklearn.metrics import f1_score

%matplotlib inline

2023-10-30 17:36:22.078914: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-10-30 17:36:22.079005: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-10-30 17:36:22.079025: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2023-10-30 17:36:22.175099: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-10-30 17:36:27.494027: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:43:00.0/numa_node
Your kernel may have been built without NUMA support.
2023-10-30 17:36:27.650172: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:43:00.0/numa_node
Your kernel may have been built without NUMA support.
2023-10-30 17:36:27.650226: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:43:00.0/numa_node
Your kernel may have been built without NUMA support.

Num GPUs Available:  1
GPU is available and being used.

Load Data (Google stock)¶

In [2]:

google_training_complete = pd.read_csv("./Google_Stock_Train (2010-2022).csv")

# Convert 'Date' column to datetime format
google_training_complete['Date'] = pd.to_datetime(google_training_complete['Date'])

google_training_complete.head(10)

Out[2]:

	Date	Open	High	Low	Close	Adj Close	Volume
0	2010-01-04	15.689439	15.753504	15.621622	15.684434	15.684434	78169752
1	2010-01-05	15.695195	15.711712	15.554054	15.615365	15.615365	120067812
2	2010-01-06	15.662162	15.662162	15.174174	15.221722	15.221722	158988852
3	2010-01-07	15.250250	15.265265	14.831081	14.867367	14.867367	256315428
4	2010-01-08	14.814815	15.096346	14.742492	15.065566	15.065566	188783028
5	2010-01-11	15.126627	15.126627	14.865866	15.042793	15.042793	288227484
6	2010-01-12	14.956206	14.968969	14.714715	14.776777	14.776777	193937868
7	2010-01-13	14.426677	14.724224	14.361862	14.691942	14.691942	259604136
8	2010-01-14	14.612112	14.869870	14.584835	14.761011	14.761011	169434396
9	2010-01-15	14.848348	14.853854	14.465465	14.514515	14.514515	217162620

Exploratory Data Analysis (EDA)¶

In [3]:

# Line chart for Google stock price over time
fig1 = px.line(google_training_complete, x='Date', y='Close', title='Google Stock Price Over Time')
fig1.show()

In [4]:

# Scatter plot of daily trading volume
fig2 = px.scatter(google_training_complete, x='Date', y='Volume', title='Daily Trading Volume')
fig2.show()

In [5]:

# Box plot of Google stock prices for each year
google_training_complete['Year'] = google_training_complete['Date'].dt.year
fig3 = px.box(google_training_complete, x='Year', y='Close', title='Google Stock Prices - Yearly Box Plot')
fig3.show()

In [7]:

# Candlestick chart for stock prices
fig4 = go.Figure(data=[go.Candlestick(x=google_training_complete['Date'],
                                     open=google_training_complete['Open'],
                                     high=google_training_complete['High'],
                                     low=google_training_complete['Low'],
                                     close=google_training_complete['Close'])])

fig4.update_layout(title='Google Stock Prices - Candlestick Chart',
                   xaxis_title='Date',
                   yaxis_title='Stock Price')

fig4.show()

In [8]:

# Histogram of daily returns
google_training_complete['Daily_Return'] = google_training_complete['Close'].pct_change()
fig5 = px.histogram(google_training_complete, x='Daily_Return', nbins=30, title='Distribution of Daily Returns')
fig5.show()

In [9]:

# Heatmap of correlation matrix
correlation_matrix = google_training_complete.corr()
fig6 = px.imshow(correlation_matrix, x=correlation_matrix.index, y=correlation_matrix.columns, title='Correlation Matrix Heatmap')
fig6.show()

In [10]:

# Moving Average of closing prices
google_training_complete['MA_50'] = google_training_complete['Close'].rolling(window=50).mean()
fig7 = px.line(google_training_complete, x='Date', y=['Close', 'MA_50'], title='Google Stock Close Price with 50-Day Moving Average')
fig7.show()

In [11]:

# Scatter plot of closing prices vs. trading volume
fig8 = px.scatter(google_training_complete, x='Close', y='Volume', title='Closing Prices vs. Trading Volume')
fig8.show()

In [12]:

# Line chart for daily stock price change
google_training_complete['Daily_Change'] = google_training_complete['Close'].diff()
fig9 = px.line(google_training_complete, x='Date', y='Daily_Change', title='Daily Stock Price Change')
fig9.show()

In [13]:

# Bar chart of trading volume per month
google_training_complete['Month'] = google_training_complete['Date'].dt.month
monthly_volume = google_training_complete.groupby('Month')['Volume'].sum().reset_index()
fig10 = px.bar(monthly_volume, x='Month', y='Volume', title='Total Trading Volume per Month')
fig10.show()

In [14]:

# Area chart for daily trading volume
fig11 = px.area(google_training_complete, x='Date', y='Volume', title='Daily Trading Volume (Area Chart)')
fig11.show()

In [15]:

# Histogram of daily stock price fluctuations
fig12 = px.histogram(google_training_complete, x='Daily_Change', title='Distribution of Daily Stock Price Changes')
fig12.show()

In [16]:

# Line chart for percent change of daily stock price
google_training_complete['Daily_Pct_Change'] = google_training_complete['Close'].pct_change() * 100
fig13 = px.line(google_training_complete, x='Date', y='Daily_Pct_Change', title='Daily Stock Price Percent Change')
fig13.show()

In [17]:

# Box plot of daily stock price percent change by month
fig14 = px.box(google_training_complete, x='Month', y='Daily_Pct_Change', title='Daily Stock Price Percent Change by Month')
fig14.show()

In [18]:

# Scatter plot of closing prices with Trend Line (should have used differnt color)
fig15 = px.scatter(google_training_complete, x='Date', y='Close', title='Google Stock Prices with Trendline', trendline='lowess')
fig15.show()

In [19]:

# Line chart for daily trading volume with moving average
google_training_complete['MA_Volume'] = google_training_complete['Volume'].rolling(window=10).mean()
fig16 = px.line(google_training_complete, x='Date', y=['Volume', 'MA_Volume'], title='Daily Trading Volume with 10-Day Moving Average')
fig16.show()

In [20]:

# Box plot of daily stock price changes by year 
google_training_complete['Year'] = google_training_complete['Date'].dt.year
fig17 = px.box(google_training_complete, x='Year', y='Daily_Change', title='Daily Stock Price Changes by Year')
fig17.show()

In [21]:

# Line chart for daily closing prices in 2022
# 2022 because Train dataset is 2010-2022
df_2022 = google_training_complete[google_training_complete['Year'] == 2022]
fig18 = px.line(google_training_complete, x='Date', y='Close', title='Google Stock Prices in 2022')
fig18.show()

In [22]:

# Scatter plot of daily returns vs. trading volume
fig19 = px.scatter(google_training_complete, x='Daily_Return', y='Volume', title='Daily Returns vs. Trading Volume')
fig19.show()

In [23]:

# Line chart for daily trading volume with range slider
fig20 = px.line(google_training_complete, x='Date', y='Volume', title='Daily Trading Volume with Range Slider')
fig20.update_xaxes(rangeslider_visible=True)
fig20.show()

Data preprocessing¶

In [24]:

google_training_processed = google_training_complete.iloc[:, 4:5].values
google_training_processed

Out[24]:

array([[15.684434],
       [15.615365],
       [15.221722],
       ...,
       [86.019997],
       [88.449997],
       [88.230003]])

In [25]:

scaler = MinMaxScaler(feature_range = (0, 1))

google_training_scaled = scaler.fit_transform(google_training_processed)

In [26]:

print(google_training_scaled)
print(google_training_scaled.shape)

[[0.03434761]
 [0.03385045]
 [0.03101697]
 ...
 [0.54062898]
 [0.55812033]
 [0.55653679]]
(3272, 1)

In [47]:

X_train = []
y_train = []
for i in range(60, google_training_scaled.shape[0]):
    X_train.append(google_training_scaled[i-60:i, 0])
    y_train.append(google_training_scaled[i, 0])

In [48]:

type(X_train)

Out[48]:

list

In [49]:

X_train, y_train = np.array(X_train), np.array(y_train)

In [50]:

X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
print(X_train.shape)

(3212, 60, 1)

LSTM layers work on 3D data with the following structure (nb_sequence, nb_timestep, nb_feature).¶

- nb_sequence : total number of sequences in dataset
- nb_timestep : size of sequences
- nb_feature : number of features describing timesteps

RNN¶

Build RNN Model¶

Pretty generic/standard RNN¶

In [51]:

RNN_model = Sequential()
RNN_model.add(SimpleRNN(units=300, return_sequences=True, input_shape=(features_set.shape[1], 1)))
RNN_model.add(Dropout(0.2))

RNN_model.add(SimpleRNN(units=100, return_sequences=True))
RNN_model.add(Dropout(0.2))

RNN_model.add(SimpleRNN(units=100, return_sequences=True))
RNN_model.add(Dropout(0.2))

RNN_model.add(SimpleRNN(units=100))
RNN_model.add(Dropout(0.2))

RNN_model.add(Dense(units = 1))

Compile RNN Model using Adam optimizer¶

In [53]:

RNN_model.summary()

Model: "sequential_3"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 simple_rnn_12 (SimpleRNN)   (None, 60, 300)           90600     
                                                                 
 dropout_12 (Dropout)        (None, 60, 300)           0         
                                                                 
 simple_rnn_13 (SimpleRNN)   (None, 60, 100)           40100     
                                                                 
 dropout_13 (Dropout)        (None, 60, 100)           0         
                                                                 
 simple_rnn_14 (SimpleRNN)   (None, 60, 100)           20100     
                                                                 
 dropout_14 (Dropout)        (None, 60, 100)           0         
                                                                 
 simple_rnn_15 (SimpleRNN)   (None, 100)               20100     
                                                                 
 dropout_15 (Dropout)        (None, 100)               0         
                                                                 
 dense_3 (Dense)             (None, 1)                 101       
                                                                 
=================================================================
Total params: 171001 (667.97 KB)
Trainable params: 171001 (667.97 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________

In [66]:

# Compiling the RNN
RNN_model.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['MAE', 'MAPE', 'F1Score', 'Accuracy', 'Precision'])
# Fitting the RNN to the Training set
RNN_History = RNN_model.fit(X_train, y_train, epochs = 200, batch_size = 640)

Epoch 1/200
6/6 [==============================] - 6s 360ms/step - loss: 0.0470 - MAE: 0.1679 - MAPE: 34665.6445 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 2/200
6/6 [==============================] - 2s 365ms/step - loss: 0.0453 - MAE: 0.1697 - MAPE: 45851.7969 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 3/200
6/6 [==============================] - 2s 356ms/step - loss: 0.0349 - MAE: 0.1435 - MAPE: 22458.0391 - f1_score: 0.4376 - Accuracy: 3.1133e-04 - precision: 1.0000
Epoch 4/200
6/6 [==============================] - 2s 349ms/step - loss: 0.0208 - MAE: 0.1146 - MAPE: 30958.8789 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 5/200
6/6 [==============================] - 2s 366ms/step - loss: 0.0276 - MAE: 0.1333 - MAPE: 16932.1641 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 6/200
6/6 [==============================] - 2s 360ms/step - loss: 0.0141 - MAE: 0.0943 - MAPE: 14159.5840 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 7/200
6/6 [==============================] - 2s 355ms/step - loss: 0.0122 - MAE: 0.0876 - MAPE: 1929.8502 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 8/200
6/6 [==============================] - 2s 355ms/step - loss: 0.0094 - MAE: 0.0770 - MAPE: 23608.6875 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 9/200
6/6 [==============================] - 2s 365ms/step - loss: 0.0085 - MAE: 0.0729 - MAPE: 2486.9617 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 10/200
6/6 [==============================] - 2s 364ms/step - loss: 0.0077 - MAE: 0.0699 - MAPE: 12808.5645 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 11/200
6/6 [==============================] - 2s 354ms/step - loss: 0.0095 - MAE: 0.0774 - MAPE: 374.5854 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 12/200
6/6 [==============================] - 2s 356ms/step - loss: 0.0084 - MAE: 0.0716 - MAPE: 4428.9150 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 13/200
6/6 [==============================] - 2s 362ms/step - loss: 0.0075 - MAE: 0.0684 - MAPE: 15339.8184 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 14/200
6/6 [==============================] - 2s 374ms/step - loss: 0.0073 - MAE: 0.0681 - MAPE: 22680.6172 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 15/200
6/6 [==============================] - 2s 348ms/step - loss: 0.0080 - MAE: 0.0710 - MAPE: 8063.8350 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 16/200
6/6 [==============================] - 2s 348ms/step - loss: 0.0067 - MAE: 0.0652 - MAPE: 32189.7090 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 17/200
6/6 [==============================] - 2s 369ms/step - loss: 0.0061 - MAE: 0.0615 - MAPE: 17116.7441 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 18/200
6/6 [==============================] - 2s 356ms/step - loss: 0.0061 - MAE: 0.0614 - MAPE: 13209.2588 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 19/200
6/6 [==============================] - 2s 347ms/step - loss: 0.0108 - MAE: 0.0849 - MAPE: 37726.6367 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 20/200
6/6 [==============================] - 2s 348ms/step - loss: 0.0092 - MAE: 0.0763 - MAPE: 37541.8242 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 21/200
6/6 [==============================] - 2s 352ms/step - loss: 0.0073 - MAE: 0.0674 - MAPE: 8676.9219 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 22/200
6/6 [==============================] - 2s 363ms/step - loss: 0.0066 - MAE: 0.0641 - MAPE: 18020.9785 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 23/200
6/6 [==============================] - 2s 350ms/step - loss: 0.0064 - MAE: 0.0627 - MAPE: 21786.7578 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 24/200
6/6 [==============================] - 2s 351ms/step - loss: 0.0057 - MAE: 0.0592 - MAPE: 16166.2402 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 25/200
6/6 [==============================] - 2s 366ms/step - loss: 0.0052 - MAE: 0.0569 - MAPE: 19620.1758 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 26/200
6/6 [==============================] - 2s 372ms/step - loss: 0.0056 - MAE: 0.0600 - MAPE: 4229.5527 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 27/200
6/6 [==============================] - 2s 354ms/step - loss: 0.0076 - MAE: 0.0695 - MAPE: 29286.2285 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 28/200
6/6 [==============================] - 2s 351ms/step - loss: 0.0069 - MAE: 0.0667 - MAPE: 3832.9861 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 29/200
6/6 [==============================] - 2s 367ms/step - loss: 0.0071 - MAE: 0.0649 - MAPE: 3955.5347 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 30/200
6/6 [==============================] - 2s 355ms/step - loss: 0.0054 - MAE: 0.0575 - MAPE: 7445.3877 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 31/200
6/6 [==============================] - 2s 385ms/step - loss: 0.0047 - MAE: 0.0532 - MAPE: 15847.2246 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 32/200
6/6 [==============================] - 2s 390ms/step - loss: 0.0042 - MAE: 0.0517 - MAPE: 4900.8691 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 33/200
6/6 [==============================] - 2s 354ms/step - loss: 0.0042 - MAE: 0.0506 - MAPE: 14455.2109 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 34/200
6/6 [==============================] - 2s 356ms/step - loss: 0.0042 - MAE: 0.0507 - MAPE: 905.5336 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 35/200
6/6 [==============================] - 2s 356ms/step - loss: 0.0052 - MAE: 0.0558 - MAPE: 6715.8965 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 36/200
6/6 [==============================] - 2s 376ms/step - loss: 0.0041 - MAE: 0.0495 - MAPE: 40924.8633 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 37/200
6/6 [==============================] - 2s 356ms/step - loss: 0.0044 - MAE: 0.0518 - MAPE: 4588.4268 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 38/200
6/6 [==============================] - 2s 413ms/step - loss: 0.0040 - MAE: 0.0497 - MAPE: 50394.9727 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 39/200
6/6 [==============================] - 2s 370ms/step - loss: 0.0041 - MAE: 0.0493 - MAPE: 1676.2240 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 40/200
6/6 [==============================] - 2s 355ms/step - loss: 0.0050 - MAE: 0.0561 - MAPE: 28412.3711 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 41/200
6/6 [==============================] - 2s 378ms/step - loss: 0.0053 - MAE: 0.0561 - MAPE: 22210.6152 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 42/200
6/6 [==============================] - 2s 353ms/step - loss: 0.0043 - MAE: 0.0507 - MAPE: 26591.7754 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 43/200
6/6 [==============================] - 2s 360ms/step - loss: 0.0042 - MAE: 0.0509 - MAPE: 18227.5840 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 44/200
6/6 [==============================] - 2s 359ms/step - loss: 0.0047 - MAE: 0.0536 - MAPE: 2750.9104 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 45/200
6/6 [==============================] - 2s 355ms/step - loss: 0.0047 - MAE: 0.0546 - MAPE: 23894.5820 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 46/200
6/6 [==============================] - 2s 350ms/step - loss: 0.0040 - MAE: 0.0499 - MAPE: 40003.7461 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 47/200
6/6 [==============================] - 2s 358ms/step - loss: 0.0040 - MAE: 0.0487 - MAPE: 14508.5820 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 48/200
6/6 [==============================] - 2s 355ms/step - loss: 0.0042 - MAE: 0.0501 - MAPE: 14071.2656 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 49/200
6/6 [==============================] - 2s 358ms/step - loss: 0.0047 - MAE: 0.0544 - MAPE: 21410.3828 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 50/200
6/6 [==============================] - 2s 360ms/step - loss: 0.0035 - MAE: 0.0463 - MAPE: 45754.6641 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 51/200
6/6 [==============================] - 2s 358ms/step - loss: 0.0045 - MAE: 0.0517 - MAPE: 18751.1797 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 52/200
6/6 [==============================] - 2s 346ms/step - loss: 0.0040 - MAE: 0.0498 - MAPE: 15332.9541 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 53/200
6/6 [==============================] - 2s 379ms/step - loss: 0.0040 - MAE: 0.0494 - MAPE: 984.3538 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 54/200
6/6 [==============================] - 2s 348ms/step - loss: 0.0034 - MAE: 0.0454 - MAPE: 26293.2305 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 55/200
6/6 [==============================] - 2s 355ms/step - loss: 0.0044 - MAE: 0.0516 - MAPE: 8264.2949 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 56/200
6/6 [==============================] - 2s 346ms/step - loss: 0.0042 - MAE: 0.0507 - MAPE: 10199.1904 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 57/200
6/6 [==============================] - 2s 360ms/step - loss: 0.0049 - MAE: 0.0537 - MAPE: 14904.8418 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 58/200
6/6 [==============================] - 2s 370ms/step - loss: 0.0039 - MAE: 0.0480 - MAPE: 107.6110 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 59/200
6/6 [==============================] - 2s 362ms/step - loss: 0.0040 - MAE: 0.0492 - MAPE: 13705.5615 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 60/200
6/6 [==============================] - 2s 359ms/step - loss: 0.0043 - MAE: 0.0507 - MAPE: 13281.3926 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 61/200
6/6 [==============================] - 2s 351ms/step - loss: 0.0045 - MAE: 0.0511 - MAPE: 2459.5901 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 62/200
6/6 [==============================] - 2s 359ms/step - loss: 0.0039 - MAE: 0.0497 - MAPE: 6559.1133 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 63/200
6/6 [==============================] - 2s 349ms/step - loss: 0.0043 - MAE: 0.0518 - MAPE: 16079.5391 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 64/200
6/6 [==============================] - 2s 353ms/step - loss: 0.0048 - MAE: 0.0548 - MAPE: 19842.4688 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 65/200
6/6 [==============================] - 2s 355ms/step - loss: 0.0034 - MAE: 0.0463 - MAPE: 22843.6289 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 66/200
6/6 [==============================] - 2s 366ms/step - loss: 0.0035 - MAE: 0.0457 - MAPE: 16522.6680 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 67/200
6/6 [==============================] - 2s 360ms/step - loss: 0.0035 - MAE: 0.0462 - MAPE: 21514.8066 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 68/200
6/6 [==============================] - 2s 356ms/step - loss: 0.0031 - MAE: 0.0434 - MAPE: 24425.9121 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 69/200
6/6 [==============================] - 2s 360ms/step - loss: 0.0030 - MAE: 0.0424 - MAPE: 6660.2129 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 70/200
6/6 [==============================] - 2s 354ms/step - loss: 0.0030 - MAE: 0.0427 - MAPE: 20068.6504 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 71/200
6/6 [==============================] - 2s 350ms/step - loss: 0.0034 - MAE: 0.0457 - MAPE: 2795.1675 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 72/200
6/6 [==============================] - 2s 353ms/step - loss: 0.0035 - MAE: 0.0466 - MAPE: 18526.0801 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 73/200
6/6 [==============================] - 2s 346ms/step - loss: 0.0032 - MAE: 0.0445 - MAPE: 2072.7393 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 74/200
6/6 [==============================] - 2s 361ms/step - loss: 0.0033 - MAE: 0.0448 - MAPE: 29976.8203 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 75/200
6/6 [==============================] - 2s 353ms/step - loss: 0.0033 - MAE: 0.0451 - MAPE: 2237.7971 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 76/200
6/6 [==============================] - 2s 355ms/step - loss: 0.0029 - MAE: 0.0424 - MAPE: 30202.9395 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 77/200
6/6 [==============================] - 2s 355ms/step - loss: 0.0027 - MAE: 0.0406 - MAPE: 9760.5537 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 78/200
6/6 [==============================] - 2s 359ms/step - loss: 0.0033 - MAE: 0.0450 - MAPE: 33296.9102 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 79/200
6/6 [==============================] - 2s 354ms/step - loss: 0.0035 - MAE: 0.0465 - MAPE: 270.2314 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 80/200
6/6 [==============================] - 2s 351ms/step - loss: 0.0034 - MAE: 0.0444 - MAPE: 2800.8938 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 81/200
6/6 [==============================] - 2s 367ms/step - loss: 0.0040 - MAE: 0.0484 - MAPE: 5511.1699 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 82/200
6/6 [==============================] - 2s 355ms/step - loss: 0.0028 - MAE: 0.0412 - MAPE: 6877.3325 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 83/200
6/6 [==============================] - 2s 353ms/step - loss: 0.0030 - MAE: 0.0433 - MAPE: 3570.9412 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 84/200
6/6 [==============================] - 2s 358ms/step - loss: 0.0035 - MAE: 0.0464 - MAPE: 38165.5625 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 85/200
6/6 [==============================] - 2s 356ms/step - loss: 0.0031 - MAE: 0.0440 - MAPE: 35574.5352 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 86/200
6/6 [==============================] - 2s 369ms/step - loss: 0.0030 - MAE: 0.0431 - MAPE: 11698.0029 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 87/200
6/6 [==============================] - 2s 367ms/step - loss: 0.0029 - MAE: 0.0418 - MAPE: 39207.3672 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 88/200
6/6 [==============================] - 2s 361ms/step - loss: 0.0028 - MAE: 0.0414 - MAPE: 15013.0771 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 89/200
6/6 [==============================] - 2s 354ms/step - loss: 0.0028 - MAE: 0.0409 - MAPE: 22587.0215 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 90/200
6/6 [==============================] - 2s 346ms/step - loss: 0.0029 - MAE: 0.0415 - MAPE: 18637.6855 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 91/200
6/6 [==============================] - 2s 363ms/step - loss: 0.0027 - MAE: 0.0401 - MAPE: 43856.3555 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 92/200
6/6 [==============================] - 2s 353ms/step - loss: 0.0028 - MAE: 0.0411 - MAPE: 22366.0078 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 93/200
6/6 [==============================] - 2s 349ms/step - loss: 0.0032 - MAE: 0.0438 - MAPE: 17855.4238 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 94/200
6/6 [==============================] - 2s 350ms/step - loss: 0.0031 - MAE: 0.0435 - MAPE: 12141.1895 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 95/200
6/6 [==============================] - 2s 349ms/step - loss: 0.0030 - MAE: 0.0430 - MAPE: 23761.3906 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 96/200
6/6 [==============================] - 2s 353ms/step - loss: 0.0028 - MAE: 0.0410 - MAPE: 8291.6162 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 97/200
6/6 [==============================] - 2s 356ms/step - loss: 0.0028 - MAE: 0.0409 - MAPE: 2062.0908 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 98/200
6/6 [==============================] - 2s 350ms/step - loss: 0.0040 - MAE: 0.0479 - MAPE: 6379.7925 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 99/200
6/6 [==============================] - 2s 350ms/step - loss: 0.0031 - MAE: 0.0443 - MAPE: 19472.6035 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 100/200
6/6 [==============================] - 2s 355ms/step - loss: 0.0028 - MAE: 0.0412 - MAPE: 3781.8198 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 101/200
6/6 [==============================] - 2s 386ms/step - loss: 0.0036 - MAE: 0.0461 - MAPE: 416.6832 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 102/200
6/6 [==============================] - 2s 359ms/step - loss: 0.0028 - MAE: 0.0411 - MAPE: 7485.9961 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 103/200
6/6 [==============================] - 2s 353ms/step - loss: 0.0027 - MAE: 0.0405 - MAPE: 4515.4707 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 104/200
6/6 [==============================] - 2s 359ms/step - loss: 0.0026 - MAE: 0.0399 - MAPE: 9387.6025 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 105/200
6/6 [==============================] - 2s 367ms/step - loss: 0.0025 - MAE: 0.0388 - MAPE: 29271.6094 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 106/200
6/6 [==============================] - 2s 356ms/step - loss: 0.0027 - MAE: 0.0404 - MAPE: 5495.4824 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 107/200
6/6 [==============================] - 2s 367ms/step - loss: 0.0025 - MAE: 0.0382 - MAPE: 24105.8457 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 108/200
6/6 [==============================] - 2s 345ms/step - loss: 0.0029 - MAE: 0.0417 - MAPE: 2152.7141 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 109/200
6/6 [==============================] - 3s 434ms/step - loss: 0.0030 - MAE: 0.0417 - MAPE: 776.0928 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 110/200
6/6 [==============================] - 2s 370ms/step - loss: 0.0024 - MAE: 0.0378 - MAPE: 24772.6309 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 111/200
6/6 [==============================] - 2s 360ms/step - loss: 0.0027 - MAE: 0.0402 - MAPE: 27523.1660 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 112/200
6/6 [==============================] - 2s 368ms/step - loss: 0.0027 - MAE: 0.0405 - MAPE: 4202.6743 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 113/200
6/6 [==============================] - 2s 350ms/step - loss: 0.0026 - MAE: 0.0393 - MAPE: 46809.0156 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 114/200
6/6 [==============================] - 2s 374ms/step - loss: 0.0025 - MAE: 0.0395 - MAPE: 7685.2764 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 115/200
6/6 [==============================] - 2s 371ms/step - loss: 0.0024 - MAE: 0.0384 - MAPE: 91.8202 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 116/200
6/6 [==============================] - 2s 369ms/step - loss: 0.0023 - MAE: 0.0376 - MAPE: 4041.5872 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 117/200
6/6 [==============================] - 2s 349ms/step - loss: 0.0024 - MAE: 0.0375 - MAPE: 5659.0024 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 118/200
6/6 [==============================] - 2s 352ms/step - loss: 0.0023 - MAE: 0.0376 - MAPE: 7982.0195 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 119/200
6/6 [==============================] - 2s 363ms/step - loss: 0.0024 - MAE: 0.0384 - MAPE: 27136.3496 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 120/200
6/6 [==============================] - 2s 355ms/step - loss: 0.0025 - MAE: 0.0387 - MAPE: 41635.8555 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 121/200
6/6 [==============================] - 2s 365ms/step - loss: 0.0024 - MAE: 0.0383 - MAPE: 5427.6929 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 122/200
6/6 [==============================] - 2s 351ms/step - loss: 0.0030 - MAE: 0.0414 - MAPE: 26344.6523 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 123/200
6/6 [==============================] - 2s 362ms/step - loss: 0.0025 - MAE: 0.0389 - MAPE: 14843.5957 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 124/200
6/6 [==============================] - 2s 397ms/step - loss: 0.0032 - MAE: 0.0451 - MAPE: 4249.5977 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 125/200
6/6 [==============================] - 2s 370ms/step - loss: 0.0033 - MAE: 0.0442 - MAPE: 14731.4326 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 126/200
6/6 [==============================] - 2s 411ms/step - loss: 0.0031 - MAE: 0.0413 - MAPE: 5009.2012 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 127/200
6/6 [==============================] - 2s 371ms/step - loss: 0.0028 - MAE: 0.0411 - MAPE: 18121.4629 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 128/200
6/6 [==============================] - 2s 359ms/step - loss: 0.0026 - MAE: 0.0396 - MAPE: 27555.6094 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 129/200
6/6 [==============================] - 2s 360ms/step - loss: 0.0023 - MAE: 0.0375 - MAPE: 5816.5649 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 130/200
6/6 [==============================] - 2s 376ms/step - loss: 0.0022 - MAE: 0.0372 - MAPE: 251.5748 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 131/200
6/6 [==============================] - 2s 371ms/step - loss: 0.0022 - MAE: 0.0364 - MAPE: 2654.7224 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 132/200
6/6 [==============================] - 2s 353ms/step - loss: 0.0023 - MAE: 0.0379 - MAPE: 7520.6104 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 133/200
6/6 [==============================] - 2s 362ms/step - loss: 0.0022 - MAE: 0.0363 - MAPE: 8246.1943 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 134/200
6/6 [==============================] - 2s 367ms/step - loss: 0.0024 - MAE: 0.0378 - MAPE: 9180.4941 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 135/200
6/6 [==============================] - 2s 373ms/step - loss: 0.0023 - MAE: 0.0373 - MAPE: 6203.1313 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 136/200
6/6 [==============================] - 2s 357ms/step - loss: 0.0023 - MAE: 0.0370 - MAPE: 1235.2007 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 137/200
6/6 [==============================] - 2s 349ms/step - loss: 0.0022 - MAE: 0.0370 - MAPE: 1517.6664 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 138/200
6/6 [==============================] - 2s 349ms/step - loss: 0.0022 - MAE: 0.0363 - MAPE: 3822.4280 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 139/200
6/6 [==============================] - 2s 360ms/step - loss: 0.0022 - MAE: 0.0364 - MAPE: 25093.9453 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 140/200
6/6 [==============================] - 2s 401ms/step - loss: 0.0022 - MAE: 0.0367 - MAPE: 22615.8730 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 141/200
6/6 [==============================] - 2s 381ms/step - loss: 0.0022 - MAE: 0.0365 - MAPE: 8926.5732 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 142/200
6/6 [==============================] - 2s 357ms/step - loss: 0.0024 - MAE: 0.0385 - MAPE: 6988.0249 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 143/200
6/6 [==============================] - 2s 355ms/step - loss: 0.0030 - MAE: 0.0430 - MAPE: 11990.0469 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 144/200
6/6 [==============================] - 2s 373ms/step - loss: 0.0031 - MAE: 0.0435 - MAPE: 6071.7891 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 145/200
6/6 [==============================] - 2s 353ms/step - loss: 0.0028 - MAE: 0.0399 - MAPE: 4435.3296 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 146/200
6/6 [==============================] - 2s 358ms/step - loss: 0.0070 - MAE: 0.0620 - MAPE: 25925.8652 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 147/200
6/6 [==============================] - 2s 362ms/step - loss: 0.0042 - MAE: 0.0500 - MAPE: 45791.1797 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 148/200
6/6 [==============================] - 2s 359ms/step - loss: 0.0069 - MAE: 0.0695 - MAPE: 17033.1250 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 149/200
6/6 [==============================] - 2s 356ms/step - loss: 0.0043 - MAE: 0.0525 - MAPE: 3669.8394 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 150/200
6/6 [==============================] - 2s 354ms/step - loss: 0.0039 - MAE: 0.0503 - MAPE: 8311.1328 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 151/200
6/6 [==============================] - 2s 357ms/step - loss: 0.0030 - MAE: 0.0435 - MAPE: 19482.1348 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 152/200
6/6 [==============================] - 2s 369ms/step - loss: 0.0031 - MAE: 0.0445 - MAPE: 7466.7310 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 153/200
6/6 [==============================] - 2s 352ms/step - loss: 0.0030 - MAE: 0.0436 - MAPE: 21220.3242 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 154/200
6/6 [==============================] - 2s 362ms/step - loss: 0.0025 - MAE: 0.0396 - MAPE: 13104.8643 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 155/200
6/6 [==============================] - 2s 387ms/step - loss: 0.0024 - MAE: 0.0388 - MAPE: 1847.1635 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 156/200
6/6 [==============================] - 2s 370ms/step - loss: 0.0023 - MAE: 0.0374 - MAPE: 2249.0518 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 157/200
6/6 [==============================] - 2s 353ms/step - loss: 0.0023 - MAE: 0.0380 - MAPE: 20992.8223 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 158/200
6/6 [==============================] - 2s 352ms/step - loss: 0.0045 - MAE: 0.0533 - MAPE: 10206.7285 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 159/200
6/6 [==============================] - 2s 359ms/step - loss: 0.0039 - MAE: 0.0477 - MAPE: 16951.2637 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 160/200
6/6 [==============================] - 2s 350ms/step - loss: 0.0038 - MAE: 0.0470 - MAPE: 1336.5027 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 161/200
6/6 [==============================] - 2s 363ms/step - loss: 0.0047 - MAE: 0.0547 - MAPE: 31755.0312 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 162/200
6/6 [==============================] - 2s 365ms/step - loss: 0.0040 - MAE: 0.0496 - MAPE: 16545.1211 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 163/200
6/6 [==============================] - 2s 375ms/step - loss: 0.0041 - MAE: 0.0495 - MAPE: 1979.2913 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 164/200
6/6 [==============================] - 2s 366ms/step - loss: 0.0035 - MAE: 0.0453 - MAPE: 5976.0303 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 165/200
6/6 [==============================] - 2s 382ms/step - loss: 0.0028 - MAE: 0.0414 - MAPE: 8878.8398 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 166/200
6/6 [==============================] - 2s 356ms/step - loss: 0.0028 - MAE: 0.0413 - MAPE: 5263.0044 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 167/200
6/6 [==============================] - 2s 364ms/step - loss: 0.0025 - MAE: 0.0386 - MAPE: 275.6794 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 168/200
6/6 [==============================] - 2s 370ms/step - loss: 0.0026 - MAE: 0.0389 - MAPE: 12066.6797 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 169/200
6/6 [==============================] - 2s 356ms/step - loss: 0.0029 - MAE: 0.0411 - MAPE: 5819.5903 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 170/200
6/6 [==============================] - 2s 373ms/step - loss: 0.0027 - MAE: 0.0406 - MAPE: 7835.0835 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 171/200
6/6 [==============================] - 2s 378ms/step - loss: 0.0026 - MAE: 0.0404 - MAPE: 3115.9006 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 172/200
6/6 [==============================] - 2s 341ms/step - loss: 0.0024 - MAE: 0.0378 - MAPE: 17391.0430 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 173/200
6/6 [==============================] - 2s 348ms/step - loss: 0.0024 - MAE: 0.0384 - MAPE: 5364.2559 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 174/200
6/6 [==============================] - 2s 353ms/step - loss: 0.0026 - MAE: 0.0401 - MAPE: 19596.7070 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 175/200
6/6 [==============================] - 2s 358ms/step - loss: 0.0024 - MAE: 0.0379 - MAPE: 13125.5215 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 176/200
6/6 [==============================] - 2s 363ms/step - loss: 0.0024 - MAE: 0.0377 - MAPE: 1499.0759 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 177/200
6/6 [==============================] - 3s 520ms/step - loss: 0.0023 - MAE: 0.0372 - MAPE: 1064.7485 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 178/200
6/6 [==============================] - 3s 379ms/step - loss: 0.0022 - MAE: 0.0361 - MAPE: 11207.5977 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 179/200
6/6 [==============================] - 2s 361ms/step - loss: 0.0024 - MAE: 0.0377 - MAPE: 6049.3574 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 180/200
6/6 [==============================] - 2s 391ms/step - loss: 0.0022 - MAE: 0.0371 - MAPE: 16938.8086 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 181/200
6/6 [==============================] - 2s 387ms/step - loss: 0.0024 - MAE: 0.0382 - MAPE: 18849.2930 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 182/200
6/6 [==============================] - 2s 418ms/step - loss: 0.0029 - MAE: 0.0419 - MAPE: 10983.9473 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 183/200
6/6 [==============================] - 2s 360ms/step - loss: 0.0031 - MAE: 0.0435 - MAPE: 13567.4297 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 184/200
6/6 [==============================] - 2s 363ms/step - loss: 0.0023 - MAE: 0.0368 - MAPE: 1034.2555 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 185/200
6/6 [==============================] - 2s 354ms/step - loss: 0.0025 - MAE: 0.0384 - MAPE: 23139.3633 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 186/200
6/6 [==============================] - 2s 351ms/step - loss: 0.0020 - MAE: 0.0347 - MAPE: 1262.4810 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 187/200
6/6 [==============================] - 2s 354ms/step - loss: 0.0020 - MAE: 0.0349 - MAPE: 10446.3057 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 188/200
6/6 [==============================] - 2s 353ms/step - loss: 0.0022 - MAE: 0.0364 - MAPE: 1684.0332 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 189/200
6/6 [==============================] - 2s 368ms/step - loss: 0.0021 - MAE: 0.0363 - MAPE: 8245.3203 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 190/200
6/6 [==============================] - 2s 352ms/step - loss: 0.0021 - MAE: 0.0358 - MAPE: 4371.4194 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 191/200
6/6 [==============================] - 2s 369ms/step - loss: 0.0022 - MAE: 0.0353 - MAPE: 223.7309 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 192/200
6/6 [==============================] - 2s 358ms/step - loss: 0.0024 - MAE: 0.0374 - MAPE: 4914.2168 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 193/200
6/6 [==============================] - 2s 376ms/step - loss: 0.0023 - MAE: 0.0367 - MAPE: 19955.9941 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 194/200
6/6 [==============================] - 2s 360ms/step - loss: 0.0050 - MAE: 0.0548 - MAPE: 9206.4229 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 195/200
6/6 [==============================] - 2s 381ms/step - loss: 0.0037 - MAE: 0.0476 - MAPE: 690.3354 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 196/200
6/6 [==============================] - 3s 432ms/step - loss: 0.0035 - MAE: 0.0464 - MAPE: 10861.7197 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 197/200
6/6 [==============================] - 2s 361ms/step - loss: 0.0028 - MAE: 0.0415 - MAPE: 2500.8582 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 198/200
6/6 [==============================] - 2s 381ms/step - loss: 0.0031 - MAE: 0.0436 - MAPE: 16417.8750 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 199/200
6/6 [==============================] - 2s 358ms/step - loss: 0.0026 - MAE: 0.0400 - MAPE: 1819.0098 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 200/200
6/6 [==============================] - 2s 354ms/step - loss: 0.0024 - MAE: 0.0385 - MAPE: 1347.4576 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000

Seems like we get a MAE of 0.09. Don't know why MAPE is all over the place.¶

Fit RNN Model¶

The thing with RNNs are that they are limited by vanishing or exploding gradients. This is whey we can use LSTM and/or GRU¶

LSTM¶

This works by passing info to the input gate, the output coming from the output gate, and the forget gate that trashes info we don't need.¶

Build LSTM Model¶

In [56]:

LSTM_model = Sequential()
LSTM_model.add(LSTM(units=300, return_sequences=True, input_shape=(features_set.shape[1], 1)))
LSTM_model.add(Dropout(0.2))

LSTM_model.add(LSTM(units=100, return_sequences=True))
LSTM_model.add(Dropout(0.2))

LSTM_model.add(LSTM(units=100, return_sequences=True))
LSTM_model.add(Dropout(0.2))

LSTM_model.add(LSTM(units=100))
LSTM_model.add(Dropout(0.2))

LSTM_model.add(Dense(units = 1))

In [57]:

LSTM_model.summary()

Model: "sequential_4"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 lstm (LSTM)                 (None, 60, 300)           362400    
                                                                 
 dropout_16 (Dropout)        (None, 60, 300)           0         
                                                                 
 lstm_1 (LSTM)               (None, 60, 100)           160400    
                                                                 
 dropout_17 (Dropout)        (None, 60, 100)           0         
                                                                 
 lstm_2 (LSTM)               (None, 60, 100)           80400     
                                                                 
 dropout_18 (Dropout)        (None, 60, 100)           0         
                                                                 
 lstm_3 (LSTM)               (None, 100)               80400     
                                                                 
 dropout_19 (Dropout)        (None, 100)               0         
                                                                 
 dense_4 (Dense)             (None, 1)                 101       
                                                                 
=================================================================
Total params: 683701 (2.61 MB)
Trainable params: 683701 (2.61 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________

Compile LSTM Model using Adam Optimizer¶

In [58]:

LSTM_model.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['MAE', 'MAPE', 'F1Score', 'Accuracy', 'Precision'])

Fit LSTM Model¶

In [59]:

LSTM_History = LSTM_model.fit(features_set, labels, epochs = 200, batch_size = 640)

Epoch 1/200
6/6 [==============================] - 7s 41ms/step - loss: 0.0867 - MAE: 0.2076 - MAPE: 10095.1367 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 2/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0540 - MAE: 0.1596 - MAPE: 16152.6367 - f1_score: 0.4376 - Accuracy: 3.1133e-04 - precision: 0.0000e+00
Epoch 3/200
6/6 [==============================] - 0s 39ms/step - loss: 0.0189 - MAE: 0.1139 - MAPE: 48786.0312 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 4/200
6/6 [==============================] - 0s 39ms/step - loss: 0.0089 - MAE: 0.0668 - MAPE: 22915.8906 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 5/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0039 - MAE: 0.0397 - MAPE: 9428.9629 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 6/200
6/6 [==============================] - 0s 39ms/step - loss: 0.0030 - MAE: 0.0381 - MAPE: 3659.3215 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 7/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0030 - MAE: 0.0390 - MAPE: 2380.5793 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 8/200
6/6 [==============================] - 0s 40ms/step - loss: 0.0023 - MAE: 0.0303 - MAPE: 6324.5840 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 9/200
6/6 [==============================] - 0s 39ms/step - loss: 0.0022 - MAE: 0.0268 - MAPE: 3557.6655 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 10/200
6/6 [==============================] - 0s 39ms/step - loss: 0.0016 - MAE: 0.0247 - MAPE: 4105.1680 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 11/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0016 - MAE: 0.0249 - MAPE: 2091.3821 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 12/200
6/6 [==============================] - 0s 39ms/step - loss: 0.0017 - MAE: 0.0247 - MAPE: 163.1446 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 13/200
6/6 [==============================] - 0s 39ms/step - loss: 0.0019 - MAE: 0.0267 - MAPE: 3607.2119 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 14/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0016 - MAE: 0.0249 - MAPE: 8090.6772 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 15/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0019 - MAE: 0.0264 - MAPE: 4008.1765 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 16/200
6/6 [==============================] - 0s 41ms/step - loss: 0.0020 - MAE: 0.0275 - MAPE: 85.4819 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 17/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0018 - MAE: 0.0269 - MAPE: 6041.6577 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 18/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0016 - MAE: 0.0238 - MAPE: 2586.8306 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 19/200
6/6 [==============================] - 0s 39ms/step - loss: 0.0013 - MAE: 0.0225 - MAPE: 3722.1621 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 20/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0014 - MAE: 0.0224 - MAPE: 4912.9385 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 21/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0013 - MAE: 0.0217 - MAPE: 4805.6602 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 22/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0013 - MAE: 0.0219 - MAPE: 1637.5371 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 23/200
6/6 [==============================] - 0s 39ms/step - loss: 0.0013 - MAE: 0.0218 - MAPE: 5341.2231 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 24/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0012 - MAE: 0.0216 - MAPE: 5495.4883 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 25/200
6/6 [==============================] - 0s 39ms/step - loss: 0.0013 - MAE: 0.0220 - MAPE: 4864.3281 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 26/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0012 - MAE: 0.0209 - MAPE: 5390.5596 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 27/200
6/6 [==============================] - 0s 39ms/step - loss: 0.0012 - MAE: 0.0210 - MAPE: 5062.6060 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 28/200
6/6 [==============================] - 0s 40ms/step - loss: 0.0012 - MAE: 0.0205 - MAPE: 5430.0830 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 29/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0011 - MAE: 0.0209 - MAPE: 6807.1172 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 30/200
6/6 [==============================] - 0s 40ms/step - loss: 0.0012 - MAE: 0.0209 - MAPE: 4940.2354 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 31/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0013 - MAE: 0.0219 - MAPE: 6652.1577 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 32/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0012 - MAE: 0.0210 - MAPE: 61.5209 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 33/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0012 - MAE: 0.0213 - MAPE: 4900.0029 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 34/200
6/6 [==============================] - 0s 39ms/step - loss: 0.0012 - MAE: 0.0208 - MAPE: 4871.5571 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 35/200
6/6 [==============================] - 0s 39ms/step - loss: 0.0012 - MAE: 0.0207 - MAPE: 5978.1606 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 36/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0010 - MAE: 0.0197 - MAPE: 3203.8962 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 37/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0012 - MAE: 0.0207 - MAPE: 4837.1782 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 38/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0011 - MAE: 0.0197 - MAPE: 2539.6870 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 39/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0015 - MAE: 0.0232 - MAPE: 2253.7524 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 40/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0014 - MAE: 0.0231 - MAPE: 7767.3105 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 41/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0012 - MAE: 0.0209 - MAPE: 6212.0278 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 42/200
6/6 [==============================] - 0s 39ms/step - loss: 0.0012 - MAE: 0.0209 - MAPE: 1497.1174 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 43/200
6/6 [==============================] - 0s 40ms/step - loss: 0.0011 - MAE: 0.0201 - MAPE: 6126.6626 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 44/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0011 - MAE: 0.0204 - MAPE: 6229.5977 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 45/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0012 - MAE: 0.0213 - MAPE: 1976.7722 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 46/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0011 - MAE: 0.0209 - MAPE: 5041.8188 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 47/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0012 - MAE: 0.0209 - MAPE: 701.9355 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 48/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0012 - MAE: 0.0210 - MAPE: 1825.7566 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 49/200
6/6 [==============================] - 0s 40ms/step - loss: 0.0013 - MAE: 0.0216 - MAPE: 3769.0562 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 50/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0012 - MAE: 0.0219 - MAPE: 923.1461 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 51/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0011 - MAE: 0.0204 - MAPE: 5427.5674 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 52/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0010 - MAE: 0.0197 - MAPE: 5282.3008 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 53/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0011 - MAE: 0.0199 - MAPE: 3953.0986 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 54/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0012 - MAE: 0.0203 - MAPE: 2343.1533 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 55/200
6/6 [==============================] - 0s 60ms/step - loss: 0.0011 - MAE: 0.0206 - MAPE: 2597.8992 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 56/200
6/6 [==============================] - 0s 48ms/step - loss: 0.0013 - MAE: 0.0215 - MAPE: 4493.4839 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 57/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0011 - MAE: 0.0201 - MAPE: 2589.6489 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 58/200
6/6 [==============================] - 0s 36ms/step - loss: 0.0010 - MAE: 0.0197 - MAPE: 4832.6577 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 59/200
6/6 [==============================] - 0s 40ms/step - loss: 0.0011 - MAE: 0.0199 - MAPE: 4441.9497 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 60/200
6/6 [==============================] - 0s 39ms/step - loss: 0.0011 - MAE: 0.0201 - MAPE: 2968.6094 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 61/200
6/6 [==============================] - 0s 41ms/step - loss: 0.0012 - MAE: 0.0204 - MAPE: 2492.5974 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 62/200
6/6 [==============================] - 0s 40ms/step - loss: 0.0010 - MAE: 0.0194 - MAPE: 1660.6082 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 63/200
6/6 [==============================] - 0s 39ms/step - loss: 9.8934e-04 - MAE: 0.0192 - MAPE: 4890.2866 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 64/200
6/6 [==============================] - 0s 40ms/step - loss: 0.0011 - MAE: 0.0200 - MAPE: 63.2859 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 65/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0010 - MAE: 0.0194 - MAPE: 4916.3887 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 66/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0012 - MAE: 0.0208 - MAPE: 4313.2866 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 67/200
6/6 [==============================] - 0s 39ms/step - loss: 0.0010 - MAE: 0.0196 - MAPE: 3751.0901 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 68/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0011 - MAE: 0.0199 - MAPE: 5368.3691 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 69/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0011 - MAE: 0.0204 - MAPE: 2105.2786 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 70/200
6/6 [==============================] - 0s 39ms/step - loss: 9.6978e-04 - MAE: 0.0192 - MAPE: 6508.0161 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 71/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0015 - MAE: 0.0240 - MAPE: 2889.1401 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 72/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0014 - MAE: 0.0226 - MAPE: 3465.6016 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 73/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0015 - MAE: 0.0232 - MAPE: 3379.9045 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 74/200
6/6 [==============================] - 0s 40ms/step - loss: 0.0012 - MAE: 0.0205 - MAPE: 2614.0823 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 75/200
6/6 [==============================] - 0s 50ms/step - loss: 9.6950e-04 - MAE: 0.0189 - MAPE: 5975.8213 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 76/200
6/6 [==============================] - 0s 42ms/step - loss: 9.8108e-04 - MAE: 0.0189 - MAPE: 1968.7216 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 77/200
6/6 [==============================] - 0s 40ms/step - loss: 9.9354e-04 - MAE: 0.0195 - MAPE: 463.7607 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 78/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0012 - MAE: 0.0209 - MAPE: 4750.7979 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 79/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0012 - MAE: 0.0204 - MAPE: 1982.2397 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 80/200
6/6 [==============================] - 0s 37ms/step - loss: 9.4675e-04 - MAE: 0.0192 - MAPE: 4735.6821 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 81/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0012 - MAE: 0.0204 - MAPE: 1293.3907 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 82/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0011 - MAE: 0.0199 - MAPE: 954.0157 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 83/200
6/6 [==============================] - 0s 38ms/step - loss: 9.6781e-04 - MAE: 0.0193 - MAPE: 5054.5200 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 84/200
6/6 [==============================] - 0s 39ms/step - loss: 0.0011 - MAE: 0.0207 - MAPE: 3812.1785 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 85/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0010 - MAE: 0.0196 - MAPE: 2060.8857 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 86/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0011 - MAE: 0.0194 - MAPE: 4462.6313 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 87/200
6/6 [==============================] - 0s 37ms/step - loss: 9.8494e-04 - MAE: 0.0192 - MAPE: 9198.7549 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 88/200
6/6 [==============================] - 0s 38ms/step - loss: 9.8756e-04 - MAE: 0.0193 - MAPE: 2768.4346 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 89/200
6/6 [==============================] - 0s 37ms/step - loss: 9.6744e-04 - MAE: 0.0189 - MAPE: 2721.3455 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 90/200
6/6 [==============================] - 0s 36ms/step - loss: 9.5425e-04 - MAE: 0.0196 - MAPE: 817.0126 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 91/200
6/6 [==============================] - 0s 38ms/step - loss: 9.5588e-04 - MAE: 0.0188 - MAPE: 517.2844 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 92/200
6/6 [==============================] - 0s 37ms/step - loss: 9.5272e-04 - MAE: 0.0189 - MAPE: 1028.2313 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 93/200
6/6 [==============================] - 0s 38ms/step - loss: 9.5845e-04 - MAE: 0.0190 - MAPE: 47.5495 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 94/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0017 - MAE: 0.0257 - MAPE: 3481.3135 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 95/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0013 - MAE: 0.0232 - MAPE: 5777.4321 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 96/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0011 - MAE: 0.0201 - MAPE: 1285.8923 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 97/200
6/6 [==============================] - 0s 41ms/step - loss: 0.0011 - MAE: 0.0202 - MAPE: 2016.4852 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 98/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0011 - MAE: 0.0204 - MAPE: 5752.2578 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 99/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0011 - MAE: 0.0197 - MAPE: 325.6953 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 100/200
6/6 [==============================] - 0s 38ms/step - loss: 9.7380e-04 - MAE: 0.0191 - MAPE: 4917.5029 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 101/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0014 - MAE: 0.0227 - MAPE: 3660.3403 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 102/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0011 - MAE: 0.0200 - MAPE: 3090.5676 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 103/200
6/6 [==============================] - 0s 37ms/step - loss: 9.5398e-04 - MAE: 0.0187 - MAPE: 3836.9253 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 104/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0010 - MAE: 0.0191 - MAPE: 2966.6067 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 105/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0011 - MAE: 0.0200 - MAPE: 3397.6150 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 106/200
6/6 [==============================] - 0s 37ms/step - loss: 9.6700e-04 - MAE: 0.0189 - MAPE: 3726.1936 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 107/200
6/6 [==============================] - 0s 38ms/step - loss: 9.5150e-04 - MAE: 0.0186 - MAPE: 877.1010 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 108/200
6/6 [==============================] - 0s 39ms/step - loss: 9.8270e-04 - MAE: 0.0193 - MAPE: 783.3370 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 109/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0010 - MAE: 0.0199 - MAPE: 5827.5225 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 110/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0011 - MAE: 0.0197 - MAPE: 1584.1986 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 111/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0011 - MAE: 0.0196 - MAPE: 2797.4688 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 112/200
6/6 [==============================] - 0s 39ms/step - loss: 0.0014 - MAE: 0.0226 - MAPE: 2340.7922 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 113/200
6/6 [==============================] - 0s 39ms/step - loss: 0.0016 - MAE: 0.0249 - MAPE: 4119.8203 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 114/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0010 - MAE: 0.0203 - MAPE: 1218.5309 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 115/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0010 - MAE: 0.0191 - MAPE: 7041.8491 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 116/200
6/6 [==============================] - 0s 37ms/step - loss: 9.2809e-04 - MAE: 0.0186 - MAPE: 1904.6056 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 117/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0010 - MAE: 0.0202 - MAPE: 5280.6240 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 118/200
6/6 [==============================] - 0s 38ms/step - loss: 9.5859e-04 - MAE: 0.0186 - MAPE: 491.9634 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 119/200
6/6 [==============================] - 0s 37ms/step - loss: 8.0834e-04 - MAE: 0.0173 - MAPE: 3212.0928 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 120/200
6/6 [==============================] - 0s 38ms/step - loss: 8.6738e-04 - MAE: 0.0179 - MAPE: 245.4634 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 121/200
6/6 [==============================] - 0s 37ms/step - loss: 8.9225e-04 - MAE: 0.0179 - MAPE: 1983.2170 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 122/200
6/6 [==============================] - 0s 39ms/step - loss: 8.6599e-04 - MAE: 0.0185 - MAPE: 1007.9837 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 123/200
6/6 [==============================] - 0s 40ms/step - loss: 0.0011 - MAE: 0.0208 - MAPE: 5557.0718 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 124/200
6/6 [==============================] - 0s 38ms/step - loss: 8.6013e-04 - MAE: 0.0185 - MAPE: 2880.4448 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 125/200
6/6 [==============================] - 0s 37ms/step - loss: 8.0012e-04 - MAE: 0.0173 - MAPE: 5518.3735 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 126/200
6/6 [==============================] - 0s 37ms/step - loss: 8.8179e-04 - MAE: 0.0177 - MAPE: 1784.5487 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 127/200
6/6 [==============================] - 0s 38ms/step - loss: 9.2242e-04 - MAE: 0.0184 - MAPE: 3962.7512 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 128/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0011 - MAE: 0.0207 - MAPE: 3693.6699 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 129/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0010 - MAE: 0.0201 - MAPE: 6704.6060 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 130/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0011 - MAE: 0.0205 - MAPE: 1773.8242 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 131/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0015 - MAE: 0.0233 - MAPE: 3853.5942 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 132/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0016 - MAE: 0.0278 - MAPE: 1887.5865 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 133/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0011 - MAE: 0.0219 - MAPE: 6183.1787 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 134/200
6/6 [==============================] - 0s 39ms/step - loss: 0.0011 - MAE: 0.0209 - MAPE: 48.8577 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 135/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0011 - MAE: 0.0211 - MAPE: 2983.3794 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 136/200
6/6 [==============================] - 0s 37ms/step - loss: 8.9494e-04 - MAE: 0.0188 - MAPE: 5246.8760 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 137/200
6/6 [==============================] - 0s 37ms/step - loss: 8.6244e-04 - MAE: 0.0180 - MAPE: 3020.9626 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 138/200
6/6 [==============================] - 0s 38ms/step - loss: 8.9431e-04 - MAE: 0.0186 - MAPE: 1229.3715 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 139/200
6/6 [==============================] - 0s 38ms/step - loss: 8.1995e-04 - MAE: 0.0183 - MAPE: 230.6032 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 140/200
6/6 [==============================] - 0s 38ms/step - loss: 9.2162e-04 - MAE: 0.0183 - MAPE: 5006.6987 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 141/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0010 - MAE: 0.0193 - MAPE: 132.6137 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 142/200
6/6 [==============================] - 0s 37ms/step - loss: 9.1031e-04 - MAE: 0.0184 - MAPE: 2424.7290 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 143/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0011 - MAE: 0.0205 - MAPE: 2858.3340 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 144/200
6/6 [==============================] - 0s 39ms/step - loss: 9.8727e-04 - MAE: 0.0188 - MAPE: 3365.1785 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 145/200
6/6 [==============================] - 0s 38ms/step - loss: 9.1864e-04 - MAE: 0.0186 - MAPE: 2401.9607 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 146/200
6/6 [==============================] - 0s 38ms/step - loss: 8.3845e-04 - MAE: 0.0175 - MAPE: 8599.2080 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 147/200
6/6 [==============================] - 0s 38ms/step - loss: 8.6452e-04 - MAE: 0.0180 - MAPE: 2882.7532 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 148/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0010 - MAE: 0.0191 - MAPE: 2820.4556 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 149/200
6/6 [==============================] - 0s 38ms/step - loss: 8.6740e-04 - MAE: 0.0179 - MAPE: 1023.7833 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 150/200
6/6 [==============================] - 0s 37ms/step - loss: 8.7193e-04 - MAE: 0.0177 - MAPE: 877.7140 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 151/200
6/6 [==============================] - 0s 39ms/step - loss: 8.2773e-04 - MAE: 0.0174 - MAPE: 2653.6829 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 152/200
6/6 [==============================] - 0s 38ms/step - loss: 8.1717e-04 - MAE: 0.0174 - MAPE: 1428.2167 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 153/200
6/6 [==============================] - 0s 38ms/step - loss: 8.9639e-04 - MAE: 0.0187 - MAPE: 4572.9272 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 154/200
6/6 [==============================] - 0s 37ms/step - loss: 7.9461e-04 - MAE: 0.0178 - MAPE: 5106.5444 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 155/200
6/6 [==============================] - 0s 38ms/step - loss: 8.1740e-04 - MAE: 0.0184 - MAPE: 1071.5863 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 156/200
6/6 [==============================] - 0s 39ms/step - loss: 7.9413e-04 - MAE: 0.0186 - MAPE: 4252.3125 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 157/200
6/6 [==============================] - 0s 38ms/step - loss: 8.5620e-04 - MAE: 0.0184 - MAPE: 2576.7703 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 158/200
6/6 [==============================] - 0s 37ms/step - loss: 8.0380e-04 - MAE: 0.0173 - MAPE: 3733.7837 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 159/200
6/6 [==============================] - 0s 37ms/step - loss: 8.4723e-04 - MAE: 0.0177 - MAPE: 1023.9481 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 160/200
6/6 [==============================] - 0s 38ms/step - loss: 9.1409e-04 - MAE: 0.0190 - MAPE: 2525.3816 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 161/200
6/6 [==============================] - 0s 37ms/step - loss: 7.5363e-04 - MAE: 0.0173 - MAPE: 1576.7024 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 162/200
6/6 [==============================] - 0s 38ms/step - loss: 7.8568e-04 - MAE: 0.0180 - MAPE: 3474.7568 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 163/200
6/6 [==============================] - 0s 37ms/step - loss: 8.3247e-04 - MAE: 0.0177 - MAPE: 2856.5544 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 164/200
6/6 [==============================] - 0s 38ms/step - loss: 8.9754e-04 - MAE: 0.0184 - MAPE: 1865.2383 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 165/200
6/6 [==============================] - 0s 38ms/step - loss: 9.2677e-04 - MAE: 0.0188 - MAPE: 3359.3464 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 166/200
6/6 [==============================] - 0s 37ms/step - loss: 8.2711e-04 - MAE: 0.0178 - MAPE: 5258.6909 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 167/200
6/6 [==============================] - 0s 38ms/step - loss: 7.6113e-04 - MAE: 0.0169 - MAPE: 7204.0708 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 168/200
6/6 [==============================] - 0s 37ms/step - loss: 7.5891e-04 - MAE: 0.0167 - MAPE: 6330.9097 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 169/200
6/6 [==============================] - 0s 37ms/step - loss: 9.2107e-04 - MAE: 0.0182 - MAPE: 3796.4421 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 170/200
6/6 [==============================] - 0s 39ms/step - loss: 8.3887e-04 - MAE: 0.0183 - MAPE: 1292.4973 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 171/200
6/6 [==============================] - 0s 37ms/step - loss: 7.8280e-04 - MAE: 0.0176 - MAPE: 3316.2532 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 172/200
6/6 [==============================] - 0s 37ms/step - loss: 7.6619e-04 - MAE: 0.0169 - MAPE: 1133.3474 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 173/200
6/6 [==============================] - 0s 40ms/step - loss: 8.3276e-04 - MAE: 0.0173 - MAPE: 3639.8511 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 174/200
6/6 [==============================] - 0s 38ms/step - loss: 7.3815e-04 - MAE: 0.0167 - MAPE: 2199.4829 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 175/200
6/6 [==============================] - 0s 40ms/step - loss: 9.1265e-04 - MAE: 0.0185 - MAPE: 266.9843 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 176/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0017 - MAE: 0.0250 - MAPE: 1066.3867 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 177/200
6/6 [==============================] - 0s 40ms/step - loss: 0.0012 - MAE: 0.0221 - MAPE: 1066.3877 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 178/200
6/6 [==============================] - 0s 42ms/step - loss: 9.5631e-04 - MAE: 0.0203 - MAPE: 1225.5463 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 179/200
6/6 [==============================] - 0s 40ms/step - loss: 0.0011 - MAE: 0.0204 - MAPE: 8483.7998 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 180/200
6/6 [==============================] - 0s 41ms/step - loss: 9.2278e-04 - MAE: 0.0198 - MAPE: 1250.0765 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 181/200
6/6 [==============================] - 0s 39ms/step - loss: 8.0489e-04 - MAE: 0.0177 - MAPE: 7725.0996 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 182/200
6/6 [==============================] - 0s 40ms/step - loss: 8.3717e-04 - MAE: 0.0179 - MAPE: 5270.9517 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 183/200
6/6 [==============================] - 0s 38ms/step - loss: 7.4017e-04 - MAE: 0.0167 - MAPE: 2469.0720 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 184/200
6/6 [==============================] - 0s 40ms/step - loss: 6.9623e-04 - MAE: 0.0163 - MAPE: 3000.8743 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 185/200
6/6 [==============================] - 0s 40ms/step - loss: 7.3121e-04 - MAE: 0.0168 - MAPE: 2214.5830 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 186/200
6/6 [==============================] - 0s 39ms/step - loss: 7.9472e-04 - MAE: 0.0170 - MAPE: 2955.8074 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 187/200
6/6 [==============================] - 0s 40ms/step - loss: 7.6226e-04 - MAE: 0.0170 - MAPE: 6020.6431 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 188/200
6/6 [==============================] - 0s 38ms/step - loss: 8.2732e-04 - MAE: 0.0175 - MAPE: 2374.7051 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 189/200
6/6 [==============================] - 0s 39ms/step - loss: 8.0163e-04 - MAE: 0.0173 - MAPE: 1088.9432 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 190/200
6/6 [==============================] - 0s 38ms/step - loss: 7.4861e-04 - MAE: 0.0167 - MAPE: 5081.1465 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 191/200
6/6 [==============================] - 0s 60ms/step - loss: 7.3903e-04 - MAE: 0.0168 - MAPE: 4080.7527 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 192/200
6/6 [==============================] - 0s 60ms/step - loss: 7.7706e-04 - MAE: 0.0174 - MAPE: 5579.6807 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 193/200
6/6 [==============================] - 0s 54ms/step - loss: 7.2327e-04 - MAE: 0.0170 - MAPE: 7961.8931 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 194/200
6/6 [==============================] - 0s 49ms/step - loss: 9.2637e-04 - MAE: 0.0193 - MAPE: 2138.3674 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 195/200
6/6 [==============================] - 0s 59ms/step - loss: 8.9312e-04 - MAE: 0.0191 - MAPE: 4415.5425 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 196/200
6/6 [==============================] - 0s 39ms/step - loss: 8.4071e-04 - MAE: 0.0184 - MAPE: 1568.6184 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 197/200
6/6 [==============================] - 0s 44ms/step - loss: 7.9499e-04 - MAE: 0.0178 - MAPE: 2082.4395 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 198/200
6/6 [==============================] - 0s 40ms/step - loss: 7.5189e-04 - MAE: 0.0177 - MAPE: 6481.0195 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 199/200
6/6 [==============================] - 0s 38ms/step - loss: 9.4304e-04 - MAE: 0.0187 - MAPE: 1355.9016 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 200/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0010 - MAE: 0.0198 - MAPE: 2984.0625 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000

LSTM was fast. MAE of 0.0198.¶

GRU¶

Simplified version of LSTM: reset gate and update gate. The reset gate decides how much of the previous hidden state to keep, and the update gate decides how much new input to incorporate into the hidden state. It requires fewer parameters.¶

Build GRU Model¶

In [60]:

GRU_model = Sequential()
GRU_model.add(GRU(units=300, return_sequences=True, input_shape=(features_set.shape[1], 1)))
GRU_model.add(Dropout(0.2))

GRU_model.add(GRU(units=100, return_sequences=True))
GRU_model.add(Dropout(0.2))

GRU_model.add(GRU(units=100, return_sequences=True))
GRU_model.add(Dropout(0.2))

GRU_model.add(GRU(units=100))
GRU_model.add(Dropout(0.2))

GRU_model.add(Dense(units = 1))

In [61]:

GRU_model.summary()

Model: "sequential_5"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 gru (GRU)                   (None, 60, 300)           272700    
                                                                 
 dropout_20 (Dropout)        (None, 60, 300)           0         
                                                                 
 gru_1 (GRU)                 (None, 60, 100)           120600    
                                                                 
 dropout_21 (Dropout)        (None, 60, 100)           0         
                                                                 
 gru_2 (GRU)                 (None, 60, 100)           60600     
                                                                 
 dropout_22 (Dropout)        (None, 60, 100)           0         
                                                                 
 gru_3 (GRU)                 (None, 100)               60600     
                                                                 
 dropout_23 (Dropout)        (None, 100)               0         
                                                                 
 dense_5 (Dense)             (None, 1)                 101       
                                                                 
=================================================================
Total params: 514601 (1.96 MB)
Trainable params: 514601 (1.96 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________

Compile GRU Model¶

In [63]:

GRU_model.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['MAE', 'MAPE', 'F1Score', 'Accuracy', 'Precision'])

Fit GRU Model¶

In [64]:

GRU_History = GRU_model.fit(features_set, labels, epochs = 200, batch_size = 640)

Epoch 1/200
6/6 [==============================] - 6s 79ms/step - loss: 0.0598 - MAE: 0.1799 - MAPE: 42537.5625 - f1_score: 0.4376 - Accuracy: 3.1133e-04 - precision: 1.0000
Epoch 2/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0157 - MAE: 0.0818 - MAPE: 24464.4004 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 3/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0041 - MAE: 0.0472 - MAPE: 14219.3887 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 4/200
6/6 [==============================] - 0s 36ms/step - loss: 0.0039 - MAE: 0.0494 - MAPE: 6708.4600 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 5/200
6/6 [==============================] - 0s 36ms/step - loss: 0.0032 - MAE: 0.0344 - MAPE: 5434.9390 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 6/200
6/6 [==============================] - 0s 36ms/step - loss: 0.0029 - MAE: 0.0323 - MAPE: 3615.2881 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 7/200
6/6 [==============================] - 0s 36ms/step - loss: 0.0023 - MAE: 0.0324 - MAPE: 10065.0117 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 8/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0023 - MAE: 0.0324 - MAPE: 1498.1990 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 9/200
6/6 [==============================] - 0s 36ms/step - loss: 0.0018 - MAE: 0.0266 - MAPE: 1392.9135 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 10/200
6/6 [==============================] - 0s 36ms/step - loss: 0.0020 - MAE: 0.0263 - MAPE: 3212.2773 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 11/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0014 - MAE: 0.0228 - MAPE: 6089.4077 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 12/200
6/6 [==============================] - 0s 36ms/step - loss: 0.0013 - MAE: 0.0220 - MAPE: 4050.6211 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 13/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0014 - MAE: 0.0221 - MAPE: 2665.9312 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 14/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0015 - MAE: 0.0234 - MAPE: 6548.0400 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 15/200
6/6 [==============================] - 0s 36ms/step - loss: 0.0015 - MAE: 0.0237 - MAPE: 3941.4199 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 16/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0014 - MAE: 0.0225 - MAPE: 7436.8794 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 17/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0012 - MAE: 0.0210 - MAPE: 1672.8270 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 18/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0011 - MAE: 0.0205 - MAPE: 3104.4785 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 19/200
6/6 [==============================] - 0s 34ms/step - loss: 0.0012 - MAE: 0.0205 - MAPE: 429.0417 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 20/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0011 - MAE: 0.0201 - MAPE: 572.4976 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 21/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0011 - MAE: 0.0201 - MAPE: 2979.8623 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 22/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0012 - MAE: 0.0204 - MAPE: 1913.9456 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 23/200
6/6 [==============================] - 0s 36ms/step - loss: 0.0010 - MAE: 0.0193 - MAPE: 1758.8938 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 24/200
6/6 [==============================] - 0s 35ms/step - loss: 9.9857e-04 - MAE: 0.0193 - MAPE: 1965.6210 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 25/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0010 - MAE: 0.0192 - MAPE: 3942.5471 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 26/200
6/6 [==============================] - 0s 36ms/step - loss: 9.8854e-04 - MAE: 0.0190 - MAPE: 2956.6628 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 27/200
6/6 [==============================] - 0s 35ms/step - loss: 9.7701e-04 - MAE: 0.0189 - MAPE: 4067.7500 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 28/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0010 - MAE: 0.0195 - MAPE: 305.6445 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 29/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0012 - MAE: 0.0215 - MAPE: 792.9704 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 30/200
6/6 [==============================] - 0s 36ms/step - loss: 0.0013 - MAE: 0.0218 - MAPE: 4082.9443 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 31/200
6/6 [==============================] - 0s 36ms/step - loss: 0.0011 - MAE: 0.0210 - MAPE: 1310.5103 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 32/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0010 - MAE: 0.0192 - MAPE: 255.6467 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 33/200
6/6 [==============================] - 0s 36ms/step - loss: 0.0019 - MAE: 0.0282 - MAPE: 7019.6064 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 34/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0013 - MAE: 0.0233 - MAPE: 6805.7559 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 35/200
6/6 [==============================] - 0s 36ms/step - loss: 0.0017 - MAE: 0.0261 - MAPE: 763.6566 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 36/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0013 - MAE: 0.0246 - MAPE: 6694.3936 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 37/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0014 - MAE: 0.0240 - MAPE: 2347.6023 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 38/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0013 - MAE: 0.0224 - MAPE: 3337.9402 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 39/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0011 - MAE: 0.0215 - MAPE: 4986.9482 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 40/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0010 - MAE: 0.0212 - MAPE: 3307.1440 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 41/200
6/6 [==============================] - 0s 36ms/step - loss: 8.9657e-04 - MAE: 0.0195 - MAPE: 2773.0916 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 42/200
6/6 [==============================] - 0s 37ms/step - loss: 0.0021 - MAE: 0.0292 - MAPE: 5419.1543 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 43/200
6/6 [==============================] - 0s 36ms/step - loss: 0.0015 - MAE: 0.0265 - MAPE: 7172.8535 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 44/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0012 - MAE: 0.0237 - MAPE: 7824.0742 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 45/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0011 - MAE: 0.0207 - MAPE: 729.2680 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 46/200
6/6 [==============================] - 0s 34ms/step - loss: 9.2524e-04 - MAE: 0.0193 - MAPE: 2714.7883 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 47/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0013 - MAE: 0.0229 - MAPE: 7408.1724 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 48/200
6/6 [==============================] - 0s 35ms/step - loss: 9.9411e-04 - MAE: 0.0205 - MAPE: 520.9581 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 49/200
6/6 [==============================] - 0s 34ms/step - loss: 0.0011 - MAE: 0.0204 - MAPE: 774.7983 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 50/200
6/6 [==============================] - 0s 37ms/step - loss: 8.9229e-04 - MAE: 0.0190 - MAPE: 5975.3296 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 51/200
6/6 [==============================] - 0s 34ms/step - loss: 9.4661e-04 - MAE: 0.0195 - MAPE: 697.5920 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 52/200
6/6 [==============================] - 0s 34ms/step - loss: 8.4952e-04 - MAE: 0.0183 - MAPE: 4304.4902 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 53/200
6/6 [==============================] - 0s 34ms/step - loss: 9.6127e-04 - MAE: 0.0194 - MAPE: 2650.1030 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 54/200
6/6 [==============================] - 0s 36ms/step - loss: 9.4477e-04 - MAE: 0.0193 - MAPE: 4256.7368 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 55/200
6/6 [==============================] - 0s 38ms/step - loss: 8.7754e-04 - MAE: 0.0186 - MAPE: 3635.0417 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 56/200
6/6 [==============================] - 0s 36ms/step - loss: 8.5041e-04 - MAE: 0.0179 - MAPE: 3296.5396 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 57/200
6/6 [==============================] - 0s 36ms/step - loss: 8.1594e-04 - MAE: 0.0180 - MAPE: 2423.9211 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 58/200
6/6 [==============================] - 0s 35ms/step - loss: 9.0585e-04 - MAE: 0.0187 - MAPE: 740.0018 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 59/200
6/6 [==============================] - 0s 35ms/step - loss: 8.9242e-04 - MAE: 0.0193 - MAPE: 3683.1199 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 60/200
6/6 [==============================] - 0s 36ms/step - loss: 8.5348e-04 - MAE: 0.0188 - MAPE: 8651.9268 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 61/200
6/6 [==============================] - 0s 35ms/step - loss: 8.2854e-04 - MAE: 0.0182 - MAPE: 3180.2783 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 62/200
6/6 [==============================] - 0s 34ms/step - loss: 9.3875e-04 - MAE: 0.0192 - MAPE: 1694.0045 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 63/200
6/6 [==============================] - 0s 34ms/step - loss: 8.5700e-04 - MAE: 0.0191 - MAPE: 8236.4512 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 64/200
6/6 [==============================] - 0s 35ms/step - loss: 8.2023e-04 - MAE: 0.0186 - MAPE: 3774.4456 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 65/200
6/6 [==============================] - 0s 34ms/step - loss: 9.7597e-04 - MAE: 0.0208 - MAPE: 2906.8979 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 66/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0015 - MAE: 0.0249 - MAPE: 5212.8018 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 67/200
6/6 [==============================] - 0s 34ms/step - loss: 9.6636e-04 - MAE: 0.0197 - MAPE: 1966.2268 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 68/200
6/6 [==============================] - 0s 35ms/step - loss: 8.9722e-04 - MAE: 0.0190 - MAPE: 7733.6567 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 69/200
6/6 [==============================] - 0s 35ms/step - loss: 9.5707e-04 - MAE: 0.0194 - MAPE: 3737.7278 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 70/200
6/6 [==============================] - 0s 35ms/step - loss: 8.7630e-04 - MAE: 0.0183 - MAPE: 4400.6309 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 71/200
6/6 [==============================] - 0s 35ms/step - loss: 7.9640e-04 - MAE: 0.0176 - MAPE: 4667.8843 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 72/200
6/6 [==============================] - 0s 35ms/step - loss: 7.6340e-04 - MAE: 0.0175 - MAPE: 759.7166 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 73/200
6/6 [==============================] - 0s 35ms/step - loss: 8.3902e-04 - MAE: 0.0178 - MAPE: 3205.3074 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 74/200
6/6 [==============================] - 0s 34ms/step - loss: 7.1532e-04 - MAE: 0.0170 - MAPE: 7425.6162 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 75/200
6/6 [==============================] - 0s 34ms/step - loss: 8.7988e-04 - MAE: 0.0191 - MAPE: 2157.1851 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 76/200
6/6 [==============================] - 0s 36ms/step - loss: 7.8703e-04 - MAE: 0.0184 - MAPE: 546.4073 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 77/200
6/6 [==============================] - 0s 34ms/step - loss: 9.4534e-04 - MAE: 0.0196 - MAPE: 3219.3984 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 78/200
6/6 [==============================] - 0s 34ms/step - loss: 8.5481e-04 - MAE: 0.0200 - MAPE: 649.7078 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 79/200
6/6 [==============================] - 0s 35ms/step - loss: 9.3607e-04 - MAE: 0.0195 - MAPE: 6181.2314 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 80/200
6/6 [==============================] - 0s 34ms/step - loss: 0.0017 - MAE: 0.0271 - MAPE: 3926.2422 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 81/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0010 - MAE: 0.0227 - MAPE: 630.2184 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 82/200
6/6 [==============================] - 0s 35ms/step - loss: 9.3932e-04 - MAE: 0.0201 - MAPE: 7741.7354 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 83/200
6/6 [==============================] - 0s 34ms/step - loss: 8.5286e-04 - MAE: 0.0183 - MAPE: 4622.4756 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 84/200
6/6 [==============================] - 0s 35ms/step - loss: 7.6727e-04 - MAE: 0.0183 - MAPE: 512.1459 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 85/200
6/6 [==============================] - 0s 35ms/step - loss: 7.4256e-04 - MAE: 0.0182 - MAPE: 6511.8423 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 86/200
6/6 [==============================] - 0s 34ms/step - loss: 7.4687e-04 - MAE: 0.0183 - MAPE: 1147.0010 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 87/200
6/6 [==============================] - 0s 35ms/step - loss: 7.7580e-04 - MAE: 0.0180 - MAPE: 4902.0366 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 88/200
6/6 [==============================] - 0s 36ms/step - loss: 7.5556e-04 - MAE: 0.0178 - MAPE: 7214.0112 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 89/200
6/6 [==============================] - 0s 35ms/step - loss: 7.7704e-04 - MAE: 0.0176 - MAPE: 4014.8123 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 90/200
6/6 [==============================] - 0s 34ms/step - loss: 7.2925e-04 - MAE: 0.0173 - MAPE: 1285.2451 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 91/200
6/6 [==============================] - 0s 35ms/step - loss: 7.5061e-04 - MAE: 0.0178 - MAPE: 4561.3784 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 92/200
6/6 [==============================] - 0s 36ms/step - loss: 7.1916e-04 - MAE: 0.0172 - MAPE: 1539.1210 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 93/200
6/6 [==============================] - 0s 39ms/step - loss: 8.1345e-04 - MAE: 0.0181 - MAPE: 5165.5171 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 94/200
6/6 [==============================] - 0s 34ms/step - loss: 7.5221e-04 - MAE: 0.0173 - MAPE: 3204.9736 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 95/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0012 - MAE: 0.0229 - MAPE: 6320.2720 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 96/200
6/6 [==============================] - 0s 36ms/step - loss: 8.9615e-04 - MAE: 0.0194 - MAPE: 2450.8765 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 97/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0011 - MAE: 0.0206 - MAPE: 193.7095 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 98/200
6/6 [==============================] - 0s 36ms/step - loss: 0.0010 - MAE: 0.0206 - MAPE: 4201.9824 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 99/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0011 - MAE: 0.0230 - MAPE: 4488.0898 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 100/200
6/6 [==============================] - 0s 34ms/step - loss: 0.0011 - MAE: 0.0239 - MAPE: 4684.4756 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 101/200
6/6 [==============================] - 0s 35ms/step - loss: 7.6561e-04 - MAE: 0.0194 - MAPE: 2994.6472 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 102/200
6/6 [==============================] - 0s 34ms/step - loss: 0.0013 - MAE: 0.0226 - MAPE: 1915.5132 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 103/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0014 - MAE: 0.0250 - MAPE: 293.3149 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 104/200
6/6 [==============================] - 0s 36ms/step - loss: 0.0012 - MAE: 0.0241 - MAPE: 1936.7085 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 105/200
6/6 [==============================] - 0s 36ms/step - loss: 9.1535e-04 - MAE: 0.0213 - MAPE: 7467.1738 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 106/200
6/6 [==============================] - 0s 34ms/step - loss: 7.6411e-04 - MAE: 0.0187 - MAPE: 416.7107 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 107/200
6/6 [==============================] - 0s 36ms/step - loss: 7.5466e-04 - MAE: 0.0178 - MAPE: 2330.8533 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 108/200
6/6 [==============================] - 0s 35ms/step - loss: 7.5015e-04 - MAE: 0.0175 - MAPE: 5318.1333 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 109/200
6/6 [==============================] - 0s 34ms/step - loss: 7.6009e-04 - MAE: 0.0173 - MAPE: 5988.5820 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 110/200
6/6 [==============================] - 0s 34ms/step - loss: 7.0994e-04 - MAE: 0.0172 - MAPE: 3324.1621 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 111/200
6/6 [==============================] - 0s 34ms/step - loss: 7.2698e-04 - MAE: 0.0175 - MAPE: 4554.6929 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 112/200
6/6 [==============================] - 0s 35ms/step - loss: 7.7762e-04 - MAE: 0.0182 - MAPE: 6328.6455 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 113/200
6/6 [==============================] - 0s 34ms/step - loss: 6.9188e-04 - MAE: 0.0176 - MAPE: 5424.5112 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 114/200
6/6 [==============================] - 0s 35ms/step - loss: 6.9595e-04 - MAE: 0.0177 - MAPE: 3765.5930 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 115/200
6/6 [==============================] - 0s 34ms/step - loss: 6.5153e-04 - MAE: 0.0164 - MAPE: 718.2709 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 116/200
6/6 [==============================] - 0s 35ms/step - loss: 7.1251e-04 - MAE: 0.0168 - MAPE: 261.5475 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 117/200
6/6 [==============================] - 0s 33ms/step - loss: 7.6156e-04 - MAE: 0.0177 - MAPE: 1907.7561 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 118/200
6/6 [==============================] - 0s 34ms/step - loss: 6.5178e-04 - MAE: 0.0165 - MAPE: 2064.2080 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 119/200
6/6 [==============================] - 0s 35ms/step - loss: 8.5291e-04 - MAE: 0.0188 - MAPE: 12962.2266 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 120/200
6/6 [==============================] - 0s 34ms/step - loss: 7.4107e-04 - MAE: 0.0173 - MAPE: 7174.8535 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 121/200
6/6 [==============================] - 0s 34ms/step - loss: 7.6460e-04 - MAE: 0.0178 - MAPE: 4190.6123 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 122/200
6/6 [==============================] - 0s 34ms/step - loss: 7.0021e-04 - MAE: 0.0176 - MAPE: 4681.1372 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 123/200
6/6 [==============================] - 0s 34ms/step - loss: 7.1405e-04 - MAE: 0.0192 - MAPE: 2401.4919 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 124/200
6/6 [==============================] - 0s 35ms/step - loss: 8.0677e-04 - MAE: 0.0179 - MAPE: 6716.6265 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 125/200
6/6 [==============================] - 0s 34ms/step - loss: 7.5553e-04 - MAE: 0.0172 - MAPE: 3859.3950 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 126/200
6/6 [==============================] - 0s 34ms/step - loss: 6.1296e-04 - MAE: 0.0160 - MAPE: 4287.5552 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 127/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0010 - MAE: 0.0209 - MAPE: 5384.1855 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 128/200
6/6 [==============================] - 0s 35ms/step - loss: 8.0929e-04 - MAE: 0.0201 - MAPE: 345.5365 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 129/200
6/6 [==============================] - 0s 34ms/step - loss: 6.6536e-04 - MAE: 0.0177 - MAPE: 2163.7483 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 130/200
6/6 [==============================] - 0s 35ms/step - loss: 6.1435e-04 - MAE: 0.0160 - MAPE: 2795.2622 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 131/200
6/6 [==============================] - 0s 36ms/step - loss: 6.6368e-04 - MAE: 0.0163 - MAPE: 7093.1025 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 132/200
6/6 [==============================] - 0s 35ms/step - loss: 6.1828e-04 - MAE: 0.0161 - MAPE: 575.1943 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 133/200
6/6 [==============================] - 0s 35ms/step - loss: 5.9749e-04 - MAE: 0.0158 - MAPE: 6105.1963 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 134/200
6/6 [==============================] - 0s 35ms/step - loss: 6.8131e-04 - MAE: 0.0165 - MAPE: 4446.3076 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 135/200
6/6 [==============================] - 0s 34ms/step - loss: 8.9191e-04 - MAE: 0.0193 - MAPE: 660.5879 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 136/200
6/6 [==============================] - 0s 34ms/step - loss: 7.5774e-04 - MAE: 0.0183 - MAPE: 3085.4739 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 137/200
6/6 [==============================] - 0s 35ms/step - loss: 7.4401e-04 - MAE: 0.0177 - MAPE: 3100.4409 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 138/200
6/6 [==============================] - 0s 34ms/step - loss: 7.6159e-04 - MAE: 0.0174 - MAPE: 1029.7885 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 139/200
6/6 [==============================] - 0s 34ms/step - loss: 7.1387e-04 - MAE: 0.0170 - MAPE: 1443.5061 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 140/200
6/6 [==============================] - 0s 34ms/step - loss: 6.5743e-04 - MAE: 0.0162 - MAPE: 1105.7280 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 141/200
6/6 [==============================] - 0s 36ms/step - loss: 6.7291e-04 - MAE: 0.0165 - MAPE: 4567.3413 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 142/200
6/6 [==============================] - 0s 34ms/step - loss: 6.2194e-04 - MAE: 0.0163 - MAPE: 9809.8701 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 143/200
6/6 [==============================] - 0s 35ms/step - loss: 6.9463e-04 - MAE: 0.0167 - MAPE: 3332.8691 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 144/200
6/6 [==============================] - 0s 35ms/step - loss: 6.6282e-04 - MAE: 0.0164 - MAPE: 4243.7505 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 145/200
6/6 [==============================] - 0s 34ms/step - loss: 6.5627e-04 - MAE: 0.0165 - MAPE: 1587.5892 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 146/200
6/6 [==============================] - 0s 34ms/step - loss: 7.1715e-04 - MAE: 0.0175 - MAPE: 391.6780 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 147/200
6/6 [==============================] - 0s 35ms/step - loss: 7.0256e-04 - MAE: 0.0177 - MAPE: 10044.3516 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 148/200
6/6 [==============================] - 0s 34ms/step - loss: 8.8631e-04 - MAE: 0.0185 - MAPE: 7914.1421 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 149/200
6/6 [==============================] - 0s 34ms/step - loss: 9.8410e-04 - MAE: 0.0218 - MAPE: 13838.8643 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 150/200
6/6 [==============================] - 0s 36ms/step - loss: 8.2050e-04 - MAE: 0.0210 - MAPE: 5233.6069 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 151/200
6/6 [==============================] - 0s 35ms/step - loss: 7.0494e-04 - MAE: 0.0182 - MAPE: 6980.9888 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 152/200
6/6 [==============================] - 0s 35ms/step - loss: 7.0280e-04 - MAE: 0.0176 - MAPE: 2383.6506 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 153/200
6/6 [==============================] - 0s 35ms/step - loss: 7.2234e-04 - MAE: 0.0169 - MAPE: 3734.5410 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 154/200
6/6 [==============================] - 0s 34ms/step - loss: 6.7157e-04 - MAE: 0.0169 - MAPE: 5528.3735 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 155/200
6/6 [==============================] - 0s 34ms/step - loss: 6.0254e-04 - MAE: 0.0159 - MAPE: 265.4604 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 156/200
6/6 [==============================] - 0s 35ms/step - loss: 7.2080e-04 - MAE: 0.0173 - MAPE: 1533.2561 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 157/200
6/6 [==============================] - 0s 35ms/step - loss: 6.9208e-04 - MAE: 0.0181 - MAPE: 5446.7056 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 158/200
6/6 [==============================] - 0s 36ms/step - loss: 6.9326e-04 - MAE: 0.0179 - MAPE: 6443.0542 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 159/200
6/6 [==============================] - 0s 34ms/step - loss: 7.2627e-04 - MAE: 0.0193 - MAPE: 8785.4453 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 160/200
6/6 [==============================] - 0s 35ms/step - loss: 9.6188e-04 - MAE: 0.0204 - MAPE: 7907.8467 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 161/200
6/6 [==============================] - 0s 38ms/step - loss: 0.0014 - MAE: 0.0249 - MAPE: 6886.6782 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 162/200
6/6 [==============================] - 0s 35ms/step - loss: 8.4995e-04 - MAE: 0.0225 - MAPE: 15876.8555 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 163/200
6/6 [==============================] - 0s 35ms/step - loss: 8.1640e-04 - MAE: 0.0204 - MAPE: 1286.1879 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 164/200
6/6 [==============================] - 0s 34ms/step - loss: 0.0010 - MAE: 0.0237 - MAPE: 7574.7168 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 165/200
6/6 [==============================] - 0s 35ms/step - loss: 8.7256e-04 - MAE: 0.0210 - MAPE: 3713.5884 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 166/200
6/6 [==============================] - 0s 34ms/step - loss: 8.5558e-04 - MAE: 0.0199 - MAPE: 10152.6641 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 167/200
6/6 [==============================] - 0s 34ms/step - loss: 7.7297e-04 - MAE: 0.0187 - MAPE: 6278.4478 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 168/200
6/6 [==============================] - 0s 35ms/step - loss: 6.9803e-04 - MAE: 0.0179 - MAPE: 1673.6584 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 169/200
6/6 [==============================] - 0s 34ms/step - loss: 6.7984e-04 - MAE: 0.0178 - MAPE: 7670.5024 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 170/200
6/6 [==============================] - 0s 35ms/step - loss: 0.0011 - MAE: 0.0220 - MAPE: 7138.9248 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 171/200
6/6 [==============================] - 0s 34ms/step - loss: 0.0013 - MAE: 0.0264 - MAPE: 5018.9126 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 172/200
6/6 [==============================] - 0s 34ms/step - loss: 0.0010 - MAE: 0.0247 - MAPE: 11362.6455 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 173/200
6/6 [==============================] - 0s 35ms/step - loss: 7.6373e-04 - MAE: 0.0202 - MAPE: 8677.2598 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 174/200
6/6 [==============================] - 0s 34ms/step - loss: 8.7156e-04 - MAE: 0.0196 - MAPE: 4196.5195 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 175/200
6/6 [==============================] - 0s 35ms/step - loss: 6.0987e-04 - MAE: 0.0165 - MAPE: 1195.7736 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 176/200
6/6 [==============================] - 0s 35ms/step - loss: 7.4348e-04 - MAE: 0.0174 - MAPE: 5786.6577 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 177/200
6/6 [==============================] - 0s 35ms/step - loss: 6.9901e-04 - MAE: 0.0182 - MAPE: 610.3708 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 178/200
6/6 [==============================] - 0s 34ms/step - loss: 7.3841e-04 - MAE: 0.0175 - MAPE: 6291.3623 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 179/200
6/6 [==============================] - 0s 34ms/step - loss: 8.0804e-04 - MAE: 0.0188 - MAPE: 8502.3154 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 180/200
6/6 [==============================] - 0s 35ms/step - loss: 6.5784e-04 - MAE: 0.0175 - MAPE: 2848.3542 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 181/200
6/6 [==============================] - 0s 35ms/step - loss: 6.0734e-04 - MAE: 0.0164 - MAPE: 2557.2864 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 182/200
6/6 [==============================] - 0s 35ms/step - loss: 5.5486e-04 - MAE: 0.0154 - MAPE: 237.8777 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 183/200
6/6 [==============================] - 0s 36ms/step - loss: 5.8076e-04 - MAE: 0.0158 - MAPE: 2085.6560 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 184/200
6/6 [==============================] - 0s 35ms/step - loss: 5.9513e-04 - MAE: 0.0159 - MAPE: 4134.6284 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 185/200
6/6 [==============================] - 0s 34ms/step - loss: 5.8860e-04 - MAE: 0.0155 - MAPE: 2329.6516 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 186/200
6/6 [==============================] - 0s 35ms/step - loss: 6.0211e-04 - MAE: 0.0159 - MAPE: 1028.7203 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 187/200
6/6 [==============================] - 0s 34ms/step - loss: 5.9954e-04 - MAE: 0.0166 - MAPE: 8056.0732 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 188/200
6/6 [==============================] - 0s 34ms/step - loss: 6.1727e-04 - MAE: 0.0160 - MAPE: 6510.5205 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 189/200
6/6 [==============================] - 0s 35ms/step - loss: 5.6278e-04 - MAE: 0.0152 - MAPE: 2023.8593 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 190/200
6/6 [==============================] - 0s 34ms/step - loss: 5.6672e-04 - MAE: 0.0155 - MAPE: 2900.2112 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 191/200
6/6 [==============================] - 0s 35ms/step - loss: 5.8325e-04 - MAE: 0.0153 - MAPE: 3035.5920 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 192/200
6/6 [==============================] - 0s 34ms/step - loss: 0.0011 - MAE: 0.0217 - MAPE: 182.1926 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 193/200
6/6 [==============================] - 0s 35ms/step - loss: 8.6783e-04 - MAE: 0.0217 - MAPE: 2149.6631 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 194/200
6/6 [==============================] - 0s 36ms/step - loss: 7.6545e-04 - MAE: 0.0191 - MAPE: 4660.5562 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 195/200
6/6 [==============================] - 0s 35ms/step - loss: 8.0095e-04 - MAE: 0.0184 - MAPE: 7045.1953 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 196/200
6/6 [==============================] - 0s 36ms/step - loss: 7.1320e-04 - MAE: 0.0175 - MAPE: 823.5317 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 197/200
6/6 [==============================] - 0s 36ms/step - loss: 8.5526e-04 - MAE: 0.0197 - MAPE: 7217.6216 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 198/200
6/6 [==============================] - 0s 34ms/step - loss: 8.9882e-04 - MAE: 0.0193 - MAPE: 6855.1128 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 199/200
6/6 [==============================] - 0s 34ms/step - loss: 8.7476e-04 - MAE: 0.0196 - MAPE: 1617.5645 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000
Epoch 200/200
6/6 [==============================] - 0s 36ms/step - loss: 6.2845e-04 - MAE: 0.0174 - MAPE: 3097.6794 - f1_score: 0.4376 - Accuracy: 6.2266e-04 - precision: 1.0000

Ok, MAE of 0.0174¶

Evaluate The Models¶

Evaluate Model Training¶

In [67]:

fig = go.Figure()

# Training loss trace
fig.add_trace(go.Scatter(x=list(range(1, len(RNN_History.history['loss']) + 1)), 
                         y=RNN_History.history['loss'],
                         mode='lines',
                         name='RNN Training Loss'))
fig.add_trace(go.Scatter(x=list(range(1, len(LSTM_History.history['loss']) + 1)), 
                         y=LSTM_History.history['loss'],
                         mode='lines',
                         name='LSTM Training Loss'))
fig.add_trace(go.Scatter(x=list(range(1, len(GRU_History.history['loss']) + 1)), 
                         y=GRU_History.history['loss'],
                         mode='lines',
                         name='GRU Training Loss'))

fig.update_layout(title='Training and Validation Loss',
                  xaxis_title='Epoch',
                  yaxis_title='Loss',
                  legend_title='Loss Type',
                  hovermode='x',
                  hoverlabel=dict(bgcolor='white', font_size=12, font_family='Rockwell'),
                  template='plotly_white')

fig.show()

Evaluate Model Prediction¶

Using cleaned test dataset¶

In [68]:

google_testing_complete = pd.read_csv("./Google_Stock_Test (2023).csv")
google_testing_processed = google_testing_complete.iloc[:, 1:2].values

google_total = pd.concat((google_training_complete['Close'], google_testing_complete['Close']), axis=0)

test_inputs = google_total[len(google_total) - len(google_testing_complete) - 60:].values
test_inputs

Out[68]:

array([101.419998,  98.68    ,  97.860001,  97.18    ,  97.559998,
        99.059998,  96.559998,  99.970001, 100.769997,  99.629997,
        99.970001, 101.129997, 102.519997, 104.480003,  94.93    ,
        92.220001,  96.290001,  94.510002,  90.470001,  86.970001,
        83.43    ,  86.580002,  88.489998,  88.900002,  87.32    ,
        93.940002,  96.410004,  95.699997,  98.440002,  98.849998,
        98.360001,  97.43    ,  95.599998,  97.050003,  98.459999,
        97.459999,  96.050003,  95.190002, 100.989998, 100.989998,
       100.440002,  99.480003,  96.980003,  94.940002,  93.709999,
        92.830002,  93.309998,  95.629997,  95.07    ,  90.860001,
        90.260002,  88.440002,  89.019997,  89.580002,  87.760002,
        89.230003,  87.389999,  86.019997,  88.449997,  88.230003,
        89.120003,  88.080002,  86.199997,  87.339996,  88.019997,
        88.419998,  91.519997,  91.129997,  92.120003,  91.290001,
        91.120003,  93.050003,  98.019997,  99.790001,  97.699997,
        95.220001,  97.519997,  99.370003,  96.940002,  98.839996,
       100.43    , 107.739998, 104.779999, 102.900002, 107.639999,
        99.370003,  95.010002,  94.57    ,  94.610001,  94.68    ,
        96.940002,  95.510002,  94.349998,  91.790001,  91.650002,
        90.889999,  89.129997,  89.870003,  90.059998,  90.360001,
        92.      ,  93.650002,  95.129997,  93.860001,  94.25    ,
        92.32    ,  90.629997,  91.110001,  93.970001,  96.110001,
       100.32    , 101.620003, 101.220001, 104.919998, 103.370003,
       105.599998, 105.440002, 102.459999, 101.029999, 101.389999,
       100.889999, 103.730003, 104.360001, 104.720001, 104.470001,
       108.419998, 106.440002, 105.349998, 104.639999, 107.43    ,
       108.870003, 105.970001, 104.5     , 104.18    , 105.290001,
       105.410004, 105.970001, 103.849998, 103.709999, 107.589996,
       107.339996, 107.199997, 105.32    , 105.410004, 104.690002,
       105.57    , 107.769997, 107.349998, 111.75    , 116.57    ,
       117.510002, 116.510002, 119.510002, 120.839996, 122.830002,
       122.760002, 125.050003, 122.559998, 120.900002, 123.480003,
       124.610001, 123.669998, 122.870003, 123.720001, 124.669998,
       126.010002, 127.309998, 122.5     , 122.139999, 122.230003,
       123.639999, 123.830002, 123.669998, 125.089996, 123.529999,
       123.099998, 120.550003, 123.150002, 122.339996, 118.339996,
       118.330002, 120.18    , 119.099998, 119.699997, 119.900002,
       121.75    , 120.110001, 119.480003, 116.449997, 117.139999,
       118.93    , 124.540001, 125.419998, 124.650002, 123.760002,
       122.029999, 119.199997, 120.019997, 121.529999, 122.209999,
       129.270004, 129.399994, 132.580002])

In [69]:

test_inputs = test_inputs.reshape(-1,1)
test_inputs = scaler.transform(test_inputs)

test_features = []
for i in range(60, len(test_inputs)):
    test_features.append(test_inputs[i-60:i, 0])
    
test_features = np.array(test_features)
test_features = np.reshape(test_features, (test_features.shape[0], test_features.shape[1], 1))
test_features.shape

Out[69]:

(143, 60, 1)

In [70]:

# Test the model on the test set
## RNN
RNN_test_loss = RNN_model.evaluate(test_features, google_testing_processed)
print(f'RNN Test Loss: {RNN_test_loss}')

## LSTM
LSTM_test_loss = LSTM_model.evaluate(test_features, google_testing_processed)
print(f'LSTM Test Loss: {LSTM_test_loss}')

## GRU
GRU_test_loss = GRU_model.evaluate(test_features, google_testing_processed)
print(f'GRU Test Loss: {GRU_test_loss}')

5/5 [==============================] - 1s 54ms/step - loss: 11554.4688 - MAE: 106.7566 - MAPE: 99.3472 - f1_score: 1.9816 - Accuracy: 0.0000e+00 - precision: 1.0000
RNN Test Loss: [11554.46875, 106.75663757324219, 99.34722137451172, array([1.9815602], dtype=float32), 0.0, 1.0]
5/5 [==============================] - 1s 27ms/step - loss: 11572.7139 - MAE: 106.8377 - MAPE: 99.4197 - f1_score: 1.9816 - Accuracy: 0.0000e+00 - precision: 1.0000
LSTM Test Loss: [11572.7138671875, 106.83771514892578, 99.41974639892578, array([1.9815602], dtype=float32), 0.0, 1.0]
5/5 [==============================] - 1s 23ms/step - loss: 11559.2441 - MAE: 106.7759 - MAPE: 99.3626 - f1_score: 1.9816 - Accuracy: 0.0000e+00 - precision: 1.0000
GRU Test Loss: [11559.244140625, 106.77589416503906, 99.36260986328125, array([1.9815602], dtype=float32), 0.0, 1.0]

Forecast Markets¶

In [71]:

# RNN
RNN_predictions = RNN_model.predict(test_features)
RNN_predictions = scaler.inverse_transform(RNN_predictions)

# LSTM
LSTM_predictions = LSTM_model.predict(test_features)
LSTM_predictions = scaler.inverse_transform(LSTM_predictions)

# GRU
GRU_predictions = GRU_model.predict(test_features)
GRU_predictions = scaler.inverse_transform(GRU_predictions)

5/5 [==============================] - 1s 54ms/step
5/5 [==============================] - 1s 10ms/step
5/5 [==============================] - 1s 12ms/step

In [72]:

google_testing_complete

Out[72]:

	Date	Open	High	Low	Close	Adj Close	Volume
0	2023-01-03	89.589996	91.050003	88.519997	89.120003	89.120003	28131200
1	2023-01-04	90.349998	90.650002	87.269997	88.080002	88.080002	34854800
2	2023-01-05	87.470001	87.570000	85.900002	86.199997	86.199997	27194400
3	2023-01-06	86.790001	87.690002	84.860001	87.339996	87.339996	41381500
4	2023-01-09	88.360001	90.050003	87.860001	88.019997	88.019997	29003900
...	...	...	...	...	...	...	...
138	2023-07-24	121.660004	123.000000	120.980003	121.529999	121.529999	29686100
139	2023-07-25	121.360001	123.150002	121.019997	122.209999	122.209999	52509600
140	2023-07-26	130.070007	130.979996	128.320007	129.270004	129.270004	61682100
141	2023-07-27	131.669998	133.240005	128.789993	129.399994	129.399994	44952100
142	2023-07-28	130.779999	133.740005	130.570007	132.580002	132.580002	36572900

143 rows × 7 columns

In [73]:

google_prediction = google_testing_complete['Date']
google_prediction = pd.DataFrame(google_prediction)
google_prediction['RNN_Open'] = RNN_predictions
google_prediction['LSTM_Open'] = LSTM_predictions
google_prediction['GRU_Open'] = GRU_predictions

google_prediction

Out[73]:

	Date	RNN_Open	LSTM_Open	GRU_Open
0	2023-01-03	89.761795	82.481285	87.833824
1	2023-01-04	89.380569	82.145378	88.074127
2	2023-01-05	89.087761	81.918633	88.152527
3	2023-01-06	88.819893	81.767494	87.754929
4	2023-01-09	88.490471	81.667305	87.387054
...	...	...	...	...
138	2023-07-24	131.086044	111.596542	120.487892
139	2023-07-25	131.083679	111.854652	120.331024
140	2023-07-26	131.231766	111.973335	120.570282
141	2023-07-27	131.598236	112.064293	121.998161
142	2023-07-28	132.154419	112.244171	123.765366

143 rows × 4 columns

In [77]:

fig = go.Figure()

# Actual Google stock price trace
fig.add_trace(go.Scatter(x=google_testing_complete.Date,
                         y=google_testing_complete.Open,
                         mode='lines',
                         name='Actual Google Stock Price',
                         line=dict(color='black')))

# RNN Google Google stock price trace
fig.add_trace(go.Scatter(x=google_prediction.Date,
                         y=google_prediction.RNN_Open,
                         mode='lines',
                         name='RNN Predicted Google Stock Price',
                         line=dict(color='red')))

# LSTM Predicted Google stock price trace
fig.add_trace(go.Scatter(x=google_prediction.Date,
                         y=google_prediction.LSTM_Open,
                         mode='lines',
                         name='LSTM Predicted Google Stock Price',
                         line=dict(color='green')))

# LSTM Predicted Google stock price trace
fig.add_trace(go.Scatter(x=google_prediction.Date,
                         y=google_prediction.GRU_Open,
                         mode='lines',
                         name='GRU Predicted Google Stock Price',
                         line=dict(color='lightblue')))

fig.update_layout(title='Google Stock Price Forecast',
                  xaxis_title='Date',
                  yaxis_title='Google Stock Price',
                  legend_title='Price Type',
                  hovermode='x',
                  hoverlabel=dict(bgcolor='white', font_size=12, font_family='Rockwell'),
                  template='plotly_white')

fig.show()

In [ ]: