Guide to Time Series Analysis with Python — 4: ARIMA and SARIMA

In previous articles, we examined analysis techniques, moving average process and autoregressive process. In this article, we will examine ARIMA (Auto Regressive Integrated Moving Average) and SARIMA (Seasonal ARIMA).

You can access previous articles here.

• Guide to Time Series Analysis with Python — 1: Analysis Techniques and Baseline Model
• Guide to Time Series Analysis with Python — 2: Moving Average Process
• Guide to Time Series Analysis with Python — 3: Autoregressive Process

You can find full code of this article on GitHub. If you are ready, let’s get started.

What is ARIMA(p,d,q)?

As the name suggests, ARIMA is a combination of AR and MA models and order of integration.

• AR: autoregressive process says that the past values in the time series affect the present.
• MA: Moving average process indicates that the current value depends on the current and past error rates
• I: Differentiation is applied to obtain a stationary time series that does not show trend or seasonality.

All of these components create the parameters to be used by ARIMA(p,d,q).