We apply fluctuation analysis, and detrended fluctuation analysis which can systematically overcome
nonstationarities in the data, to evaluate the models accordingto their ability to reproduce the proper fluctuations and trends in the past and compare the results with the future prediction.
Not exact matches
Because the preliminary examination suggested evidence of
nonstationarity and autocorrelation
in these series, models were chosen to account for such conditions accordingly.
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.
On a tangent allow me to compliment for your brilliant paper «
Nonstationarity versus scaling
in Hydrology, 2005».
Processes with consistently positive autocorrelation functions lead to large and long «excursions» from the mean as shown
in Fig. 12 (lower panel), which often tends to be interpreted as
nonstationarity.
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.
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.