Analyses for stationary versus non-stationary processes

Analyses for stationary versus non-stationary processes

This is the landing page for topics related to analyzing stationary versus non-stationary processes.

Considering the stationarity of your process is essential because it affects how patterns like means, variances, and autoregressions behave over time. Stationary processes require different modeling approaches compared to non-stationary processes. What modeling approach is appropriate for non-stationary processes depends on the characteristics that make the process non-stationary, such as trends, cycles, or shifts in behavior.

Below we have specified multiple articles where you can read more about stationary and non-stationary modeling.

Think more about stationary versus non-stationary models

Types of stationarity

The literature distinguishes between different types of stationarity.

[Mean versus trend stationarity]
[Unit root]

Testing for stationarity

There are different tests available to test for the different types of stationarity.

[Stationarity tests]
[Auto-ARIMA]

Accounting for non-stationarity

There are some techniques that can be used to account for non-stationarity in your process.

[Differencing]
[ARIMA]
[(G)ARCH]

About (non-)stationarity

Theoretical assumptions may help determine the temporal pattern of your process.