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!pip install pystan==2.19.1.1 prophet
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Requirement already satisfied: pystan==2.19.1.1 in /usr/local/lib/python3.8/dist-packages (2.19.1.1)
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Collecting cmdstanpy>=1.0.4
Downloading cmdstanpy-1.1.0-py3-none-any.whl (83 kB)
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Installing collected packages: cmdstanpy
Attempting uninstall: cmdstanpy
Found existing installation: cmdstanpy 0.9.5
Uninstalling cmdstanpy-0.9.5:
Successfully uninstalled cmdstanpy-0.9.5
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
fbprophet 0.7.1 requires cmdstanpy==0.9.5, but you have cmdstanpy 1.1.0 which is incompatible.
Successfully installed cmdstanpy-1.1.0
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import numpy as np
import pandas as pd
import tensorflow as tf
import seaborn as sns
import matplotlib.pyplot as plt
import tensorflow as tf
import os
from sklearn.preprocessing import MinMaxScaler
from prophet import Prophet
from prophet.plot import add_changepoints_to_plot, plot_plotly, plot_components_plotly
import warnings
import plotly.graph_objects as go
import plotly.express as px
warnings.filterwarnings("ignore")
sns.set_theme()
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data = pd.read_csv('/content/Final dataset - CHITTAGONG 2023-01-20 to 2023-02-18.csv')
data.head()
Out[ ]:
| Date | Time | Temparature(c) | Humidity% | |
|---|---|---|---|---|
| 0 | 2023-01-20 | 0:00:00 | 15.2 | 71.49 |
| 1 | 2023-01-20 | 1:00:00 | 16.9 | 48.94 |
| 2 | 2023-01-20 | 2:00:00 | 16.3 | 50.14 |
| 3 | 2023-01-20 | 3:00:00 | 14.2 | 71.55 |
| 4 | 2023-01-20 | 4:00:00 | 15.6 | 52.07 |
In [ ]:
# Prepare data for Prophet
days = int(7)
predicted_data = pd.DataFrame()
Prophet is a time series forecasting library developed by Facebook that allows you to make predictions based on historical time series data. Once you create an instance of the Prophet class, you can use its methods to fit the model to your data, make predictions, and visualize the results.
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model1 = Prophet()
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df_for_humidity=data[['Date', 'Humidity%']]
df_for_humidity['Date']=pd.to_datetime(df_for_humidity['Date'])
df_for_humidity.rename(columns = {'Date':'ds'}, inplace = True)
df_for_humidity.rename(columns = {'Humidity%':'y'}, inplace = True)
model1.fit(df_for_humidity)
INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this. INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
Out[ ]:
<prophet.forecaster.Prophet at 0x7f32e77d5040>
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future = model1.make_future_dataframe(periods = days)
future.tail(5)
Out[ ]:
| ds | |
|---|---|
| 32 | 2023-02-21 |
| 33 | 2023-02-22 |
| 34 | 2023-02-23 |
| 35 | 2023-02-24 |
| 36 | 2023-02-25 |
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forecast = model1.predict(future)
predicted_data['Date'] = forecast['ds']
predicted_data['Humidity%'] = forecast['yhat']
# forecast = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]
forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()
Out[ ]:
| ds | yhat | yhat_lower | yhat_upper | |
|---|---|---|---|---|
| 32 | 2023-02-21 | 51.327467 | 29.332195 | 72.490708 |
| 33 | 2023-02-22 | 55.204802 | 32.915952 | 73.969156 |
| 34 | 2023-02-23 | 54.219886 | 32.279221 | 75.889967 |
| 35 | 2023-02-24 | 56.362918 | 33.955412 | 77.824773 |
| 36 | 2023-02-25 | 53.215721 | 31.932644 | 73.995633 |
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fig = model1.plot(forecast,figsize=(26, 6))
# fig = m.plot(forecast,figsize=(20, 12))
a = add_changepoints_to_plot(fig.gca(), model1, forecast)
plt.xlabel('Date')
plt.ylabel('Humidity')
plt.tight_layout()
plt.show()
In [ ]:
data1 = pd.read_csv('/content/Final dataset - CHITTAGONG 2023-01-20 to 2023-02-18.csv')
data1.head()
Out[ ]:
| Date | Time | Temparature(c) | Humidity% | |
|---|---|---|---|---|
| 0 | 2023-01-20 | 0:00:00 | 15.2 | 71.49 |
| 1 | 2023-01-20 | 1:00:00 | 16.9 | 48.94 |
| 2 | 2023-01-20 | 2:00:00 | 16.3 | 50.14 |
| 3 | 2023-01-20 | 3:00:00 | 14.2 | 71.55 |
| 4 | 2023-01-20 | 4:00:00 | 15.6 | 52.07 |
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model2 = Prophet()
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df_for_temp=data1[['Date', 'Temparature(c)']]
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df_for_temp
Out[ ]:
| Date | Temparature(c) | |
|---|---|---|
| 0 | 2023-01-20 | 15.2 |
| 1 | 2023-01-20 | 16.9 |
| 2 | 2023-01-20 | 16.3 |
| 3 | 2023-01-20 | 14.2 |
| 4 | 2023-01-20 | 15.6 |
| ... | ... | ... |
| 715 | 2023-02-18 | 23.7 |
| 716 | 2023-02-18 | 23.7 |
| 717 | 2023-02-18 | 23.7 |
| 718 | 2023-02-18 | 22.8 |
| 719 | 2023-02-18 | 21.9 |
720 rows × 2 columns
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df_for_temp['Date']=pd.to_datetime(df_for_temp['Date'])
df_for_temp.rename(columns = {'Date':'ds'}, inplace = True)
df_for_temp.rename(columns = {'Temparature(c)': 'y'}, inplace = True)
In [ ]:
df_for_temp
Out[ ]:
| ds | y | |
|---|---|---|
| 0 | 2023-01-20 | 15.2 |
| 1 | 2023-01-20 | 16.9 |
| 2 | 2023-01-20 | 16.3 |
| 3 | 2023-01-20 | 14.2 |
| 4 | 2023-01-20 | 15.6 |
| ... | ... | ... |
| 715 | 2023-02-18 | 23.7 |
| 716 | 2023-02-18 | 23.7 |
| 717 | 2023-02-18 | 23.7 |
| 718 | 2023-02-18 | 22.8 |
| 719 | 2023-02-18 | 21.9 |
720 rows × 2 columns
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model2.fit(df_for_temp)
INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this. INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
Out[ ]:
<prophet.forecaster.Prophet at 0x7f32e5e17bb0>
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future = model2.make_future_dataframe(periods = days)
future.tail(5)
Out[ ]:
| ds | |
|---|---|
| 32 | 2023-02-21 |
| 33 | 2023-02-22 |
| 34 | 2023-02-23 |
| 35 | 2023-02-24 |
| 36 | 2023-02-25 |
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forecast = model2.predict(future)
predicted_data['Temparature(c)'] = forecast['yhat']
# forecast = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]
forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()
Out[ ]:
| ds | yhat | yhat_lower | yhat_upper | |
|---|---|---|---|---|
| 32 | 2023-02-21 | 21.268647 | 17.082549 | 25.543922 |
| 33 | 2023-02-22 | 21.520523 | 17.092971 | 25.461118 |
| 34 | 2023-02-23 | 21.861993 | 17.218035 | 26.199335 |
| 35 | 2023-02-24 | 21.044969 | 16.837732 | 25.517934 |
| 36 | 2023-02-25 | 21.416377 | 16.881256 | 25.831405 |
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fig = model2.plot(forecast,figsize=(26, 6))
# fig = m.plot(forecast,figsize=(20, 12))
a = add_changepoints_to_plot(fig.gca(), model2, forecast)
plt.xlabel('Date')
plt.ylabel('Temperature')
plt.tight_layout()
plt.show()
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predicted_data.iloc[-7:,:]
Out[ ]:
| Date | Humidity% | Temparature(c) | |
|---|---|---|---|
| 30 | 2023-02-19 | 59.496735 | 21.509351 |
| 31 | 2023-02-20 | 53.614904 | 21.717620 |
| 32 | 2023-02-21 | 51.327467 | 21.268647 |
| 33 | 2023-02-22 | 55.204802 | 21.520523 |
| 34 | 2023-02-23 | 54.219886 | 21.861993 |
| 35 | 2023-02-24 | 56.362918 | 21.044969 |
| 36 | 2023-02-25 | 53.215721 | 21.416377 |
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from sklearn import linear_model
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
ls = linear_model.LinearRegression()
X = data["Humidity%"].values.reshape(-1,1)
y = data["Temparature(c)"].values.reshape(-1,1)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
shuffle=True, random_state=0)
print("Linear Regression")
ls.fit(X_train, y_train)
print("alpha = ",ls.coef_[0])
print("beta = ",ls.intercept_)
print("\n\nCalculating some regression quality metrics")
y_pred = ls.predict(X_test)
print("MSE = ",mean_squared_error(y_test, y_pred))
print("R2 = ",r2_score(y_test, y_pred))
Linear Regression alpha = [-0.10560347] beta = [28.49799402] Calculating some regression quality metrics MSE = 8.87122800689842 R2 = 0.2617854287631435