Member-only story
DATA SCIENCE, DATA VISUALIZATION
How to Build SARIMA Model in Python
A real-life example with full code.
In this post, my goal is to give a quick tutorial on how to implement the SARIMA (Seasonal Autoregressive Integrated Moving Average) model to forecast seasonal data using python in the Jupyter notebook. If you wish to follow along, please download the data and code from my GitHub account.
Dataset
According to the U.S. Energy Information Administration, this data captures the monthly net electricity generation from coal in the country. The numbers are in Kilowatthours.
The goal of this exercise is to build a SARIMA model to forecast values till 2030.
Importing Dependencies
Now, let’s start creating the model by importing various libraries in the Jupyter notebook that would make our job easier such as NumPy, Pandas, Matplotlib, Sklearn, Itertools, Warnings and Statsmodels.
