ARIMA for Time Series Forecasting in Python

Forecasting Beer Sales with ARIMA in Python

Time Series Forecasting || ARIMA || SARIMA || Python

Time series analysis and forecasting is a tough nut to crack, but the ARIMA model has been cracking it for decades. ARIMA, short for “Auto-Regressive Integrated Moving Average,” is a powerful statistical modeling technique for time series analysis. It’s particularly effective when the time series you’re analyzing follows a clear pattern, like seasonal changes in weather or sales. ARIMA has been used to forecast everything from beer sales to order quantities, and this tutorial will show you how to build your own ARIMA model in Python. You’ll be making predictions like a pro in no time!

This article proceeds in two parts:
The first part covers the concepts behind ARIMA. You will learn how ARIMA works, what Stationarity means, and when it is appropriate to use ARIMA.
The second part is a Python hands-on applies auto-ARIMA to the Sales Forecasting domain. We’ll be working with a time series of beer sales, and our goal is to predict how the beer sales quantities will evolve in the coming years.

First, we check if the time series is stationary. Then we train an ARIMA forecasting model. Finally, we use the model to produce a…