Wavelet methods for time series analysis Andrew T. Walden, Donald B. Percival
Publisher: Cambridge University Press
Technical Note: Using wavelet analyses on water depth time series to detect glacial influence in high-mountain hydrosystems. Spectral analysis and state-space models, the text includes modern developments including categorical time series analysis, multivariate spectral methods, long memory series, nonlinear models, resampling techniques, GARCH models, stochastic volatility, wavelets and Markov chain Monte Carlo integration methods. This is a software package for the analysis of a data series using wavelet methods. Time Series Analysis and Its Applications presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. A wavelet transform is almost always implemented as a bank of filters that decompose a signal into multiple signal bands. Then I computed the strength of the strongest peak in the DCDFT spectrum over the I also analyzed the GISP2 d18O data using another popular time-frequency method, wavelet analysis (using the WWZ, Foster 1996, Astronomical J., 112, 1709). I generated 500 white-noise data series with the same time sampling as the Agassiz d18O data from 6000 to 8000 yr BP. Wavelets are a relatively new signal processing method. Dangles1,2,3 time series were acquired over the same period. Furthermore, we found that our method permits to detect glacial signal in supposedly non-glacial sites, thereby evidencing glacial meltwater infiltrations. 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. It separates and retains the signal features in one or a few of these subbands.