Detecting time series stationarity
Several time series forecasting techniques assume stationarity. This makes it essential to understand whether the time series you are working with is stationary or non-stationary.
A stationary time series implies that specific statistical properties do not vary over time and remain steady, making the processes easier to model and predict. On the other hand, a non-stationary process is more complex to model due to the dynamic nature and variations over time (for example, in the presence of trend or seasonality).
There are different approaches for defining stationarity; some are strict and may not be possible to observe in real-world data, referred to as strong stationarity. In contrast, other definitions are more modest in their criteria and can be observed in (or transformed into) real-world data, known as weak stationarity.
In this recipe, and for practical reasons, a stationary time series is defined as a time series with a constant mean...
