⏳ Time Series Forecasting with PyCaret: A Beginner-Friendly Guide
PyCaret (Part 5)
📚Chapter: — PyCaret
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Forecasting the future based on past data is one of the most exciting applications of data science. Whether you're predicting stock prices, electricity demand, or sales figures, time series forecasting helps you make data-driven decisions.
In this blog, we’ll learn how to perform time series forecasting using PyCaret, a low-code machine learning library that simplifies the entire ML pipeline. We'll walk through a simple example using a built-in dataset, so you can follow along easily.
🧰 What is PyCaret?
PyCaret is an open-source, low-code machine learning library in Python. It automates data preprocessing, model selection, and evaluation with just a few lines of code — perfect for those who want to get quick results without diving deep into every algorithm.
📦 Step 1: Install PyCaret
First, install PyCaret (if you haven’t already):
pip install pycaretFor time series tasks, you also need:
pip install pycaret[ts]📊 Step 2: Load a Built-in Dataset
### loading sample dataset from pycaret dataset module
from pycaret.datasets import get_data
data = get_data('airline')PyCaret comes with built-in datasets. We'll use the Airline dataset, which contains monthly airline passengers from 1949 to 1960.
This dataset has two columns: Month (datetime) and Passengers (number of airline passengers). It’s perfect for a basic time series example.
# plot the dataset
data.plot()⚙️ Step 3: Set Up PyCaret for Time Series
# import pycaret time series and init setup
from pycaret.time_series import *
s = setup(data, fh = 3, session_id = 123)# import TSForecastingExperiment and init the class
from pycaret.time_series import TSForecastingExperiment
exp = TSForecastingExperiment()# check the type of exp
type(exp)# check statistical tests on original data
check_stats()🤖 Step 4: Compare Models
# compare baseline models
best = compare_models()🖼️ Step 6- Analyze Model
# plot forecast
plot_model(best, plot = 'forecast')# plot forecast for 36 months in future
plot_model(best, plot = 'forecast', data_kwargs = {'fh' : 36})# residuals plot
plot_model(best, plot = 'residuals')📈 Step 5: Make Predictions
# predict on test set holdout_pred = predict_model(best)Please Follow and 👏 Subscribe for the story courses teach to see latest updates on this story
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References
1- PyCaret 101: An introduction for beginners
2-Auto_Model_Training_and_Evaluation_.ipynb
3-Build a machine learning model with PyCaret and corresponding user interface with Gradi
5- Binary Classification Tutorial Level Beginner - CLF101.ipynb
6- Pycaret