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Wavelet methods for time series analysis Andrew T. Walden, Donald B. Percival
Publisher: Cambridge University Press

No, but of course there are examples where weather and climate data time series both from observations and from climate models have been analyzed with wavelets. It separates and retains the signal features in one or a few of these subbands. Several wavelet techniques in the analysis of time series are developed and applied to real data sets. A wavelet transform is almost always implemented as a bank of filters that decompose a signal into multiple signal bands. Methods for time series analyses may be divided into two classes: frequency-domain methods and time-domain methods. Although it is not uncommon for users to log data, extract it from a file or database and then analyze it offline to modify the process, many times the changes need to happen during run time. We also fit Finally, we find that a series of damped random walk models provides a good fit to the 10Be data with a fixed characteristic time scale of 1000 years, which is roughly consistent with the quasi-periods found by the Fourier and wavelet analyses. Wavelets are a relatively new signal processing method. The complexity of the system is expressed by several parameters of nonlinear dynamics, such as embedding dimension or false nearest neighbors, and the method of delay coordinates is applied to the time series. Econometricians study time series from the point of frequency methods (spectrum analysis, wavelet analysis) and the methods of time domain (cross-correlation analysis, autocorrelation analysis). What you probably want to know is something like the average error is 1 °C or the 95% confidence interval is ±2 °C.