Literally, claims
of nonstationarity can not stand unless the evolution in time of the statistical characteristics of the process is known in deterministic terms not only for the past, but also for the future in particular.
In case anyone is left wondering why I had to devise a means of eliminating the middle man (wavelets): It's because
of the nonstationarity.
Because the preliminary examination suggested evidence
of nonstationarity and autocorrelation in these series, models were chosen to account for such conditions accordingly.
Not exact matches
«Change is not synonymous to
nonstationarity, since even an ideal stationary white noise process involves change, which however becomes less and less distinct as the time scale
of viewing the process (e.g., time scale
of averaging) increases.
However, revisiting the notions
of stationarity and
nonstationarity, which are defined within stochastics, we may understand that the terms are often abused.
The changing character
of climate, verified for the past or predicted for the future, has been sometimes described by the term
nonstationarity.
Nonstationarity of error terms is a serious problem in time - series analysis, but I don't have a good sense
of how well this issue has been treated in climate analysis.