Topic summary
Non-stationary
Extracted from the Wikipedia article Stationary process.
Techniques to stationarize a non-stationary process
In time series analysis and stochastic processes, stationarizing a time series is a crucial preprocessing step aimed at transforming a non-stationary process into a stationary one. Several techniques exist for achieving this, depending on the type and order of non-stationarity present. For first-order non-stationarity, where the mean of the process varies over time, differencing is a common and effective method: it transforms the series by subtracting each value from its predecessor, thus stabilizing the mean. For non-stationarities up to the second order, time-frequency analysis (e.g., Wavelet transform, Wigner distribution function, or Short-time Fourier transform) can be employed to isolate and suppress time-localized, non-stationary spectral components. Additionally, surrogate data methods can be used to construct strictly stationary versions of the original time series.