Overview¶

In the 10x series of notebooks, we will look at Time Series modeling in pycaret using univariate data and no exogenous variables. We will use the famous airline dataset for illustration. Our plan of action is as follows:

Perform EDA on the dataset to extract valuable insight about the process generating the time series. (Covered in this notebook)
Model the dataset based on exploratory analysis (univariable model without exogenous variables).
Use an automated approach (AutoML) to improve the performance.
User customizations, potential pitfalls and how to overcome them.

In [1]:

# Only enable critical logging (Optional)
import os
os.environ["PYCARET_CUSTOM_LOGGING_LEVEL"] = "CRITICAL"

In [2]:

def what_is_installed():
    from pycaret import show_versions
    show_versions()

try:
    what_is_installed()
except ModuleNotFoundError:
    !pip install pycaret
    what_is_installed()

System:
    python: 3.8.13 (default, Mar 28 2022, 06:59:08) [MSC v.1916 64 bit (AMD64)]
executable: C:\Users\Nikhil\.conda\envs\pycaret_dev_sktime_0p11_2\python.exe
   machine: Windows-10-10.0.19044-SP0

PyCaret required dependencies:

C:\Users\Nikhil\.conda\envs\pycaret_dev_sktime_0p11_2\lib\site-packages\_distutils_hack\__init__.py:30: UserWarning: Setuptools is replacing distutils.
  warnings.warn("Setuptools is replacing distutils.")

                 pip: 21.2.2
          setuptools: 61.2.0
             pycaret: 3.0.0
             ipython: Not installed
          ipywidgets: 7.7.0
               numpy: 1.21.6
              pandas: 1.4.2
              jinja2: 3.1.2
               scipy: 1.8.0
              joblib: 1.1.0
             sklearn: 1.0.2
                pyod: Installed but version unavailable
            imblearn: 0.9.0
   category_encoders: 2.4.1
            lightgbm: 3.3.2
               numba: 0.55.1
            requests: 2.27.1
          matplotlib: 3.5.2
          scikitplot: 0.3.7
         yellowbrick: 1.4
              plotly: 5.8.0
             kaleido: 0.2.1
         statsmodels: 0.13.2
              sktime: 0.11.4
               tbats: Installed but version unavailable
            pmdarima: 1.8.5

PyCaret optional dependencies:
                shap: Not installed
           interpret: Not installed
                umap: Not installed
    pandas_profiling: Not installed
  explainerdashboard: Not installed
             autoviz: Not installed
           fairlearn: Not installed
             xgboost: Not installed
            catboost: Not installed
              kmodes: Not installed
             mlxtend: Not installed
       statsforecast: 0.5.5
        tune_sklearn: Not installed
                 ray: Not installed
            hyperopt: Not installed
              optuna: Not installed
               skopt: Not installed
              mlflow: 1.25.1
              gradio: Not installed
             fastapi: Not installed
             uvicorn: Not installed
              m2cgen: Not installed
           evidently: Not installed
                nltk: Not installed
            pyLDAvis: Not installed
              gensim: Not installed
               spacy: Not installed
           wordcloud: Not installed
            textblob: Not installed
              psutil: 5.9.0
               fugue: Not installed
           streamlit: Not installed
             prophet: Not installed

In [3]:

import time
import numpy as np
import pandas as pd

from pycaret.datasets import get_data
from pycaret.time_series import TSForecastingExperiment

In [4]:

y = get_data('airline', verbose=False)

In [5]:

# We want to forecast the next 12 months of data and we will use 3 fold cross-validation to test the models.
fh = 12 # or alternately fh = np.arange(1,13)
fold = 3

In [6]:

# Global Plot Settings
fig_kwargs={'renderer': 'notebook'}

Exploratory Analysis¶

pycaret Time Series Forecasting module provides a conventient interface for perform exploratory analysis using plot_model.

NOTE:

Without an estimator argument, plot_model will plot using the original dataset. We will cover this in the current notebook.
If an estimator (model) is passed to plot_model, the the plots are made using the model data (e.g. future forecasts, or analysis on insample residuals). We will cover this in a subsequent notebook.

Let's see how this works next.

First, we will plots the original dataset.

In [7]:

eda = TSForecastingExperiment()
eda.setup(data=y, fh=fh, fig_kwargs=fig_kwargs)

	Description	Value
0	session_id	7961
1	Target	Number of airline passengers
2	Approach	Univariate
3	Exogenous Variables	Not Present
4	Original data shape	(144, 1)
5	Transformed data shape	(144, 1)
6	Transformed train set shape	(132, 1)
7	Transformed test set shape	(12, 1)
8	Rows with missing values	0.0%
9	Fold Generator	ExpandingWindowSplitter
10	Fold Number	3
11	Enforce Prediction Interval	False
12	Seasonal Period(s) Tested	12
13	Seasonality Present	True
14	Seasonalities Detected	[12]
15	Primary Seasonality	12
16	Target Strictly Positive	True
17	Target White Noise	No
18	Recommended d	1
19	Recommended Seasonal D	1
20	Preprocess	False
21	CPU Jobs	-1
22	Use GPU	False
23	Log Experiment	False
24	Experiment Name	ts-default-name
25	USI	8a03

Out[7]:

<pycaret.time_series.forecasting.oop.TSForecastingExperiment at 0x170810b23d0>

In [8]:

# NOTE: This is the same as `eda.plot_model(plot="ts")`
eda.plot_model()  