Making a time series stationary

A non-stationary time series data is likely to be affected by a trend or seasonality. Trending time series data has a mean that is not constant over time. Data that is affected by seasonality have variations at specific intervals in time. In making a time series data stationary, the trend and seasonality effects have to be removed. Detrending, differencing, and decomposition are such methods. The resulting stationary data is then suitable for statistical forecasting.

Let's look at all three methods in detail.

Detrending

The process of removing a trend line from a non-stationary data is known as detrending. This involves a transformation step that normalizes large values into smaller ones...