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

Furthermore, we found that our method permits to detect glacial signal in supposedly non-glacial sites, thereby evidencing glacial meltwater infiltrations. Summary: Wavelet-based morphometry (WBM) is an alternative strategy to voxel-based morphometry (VBM) consisting in conducting the statistical analysis (i.e., univariate tests) in the wavelet domain. This method derives images of functional neural networks from singular-value decomposition of BOLD signal time series, and allows derivation of images when the analyzed BOLD signal is constrained to the scans occurring in peristimulus time, using all other scans as baseline. Its wavelet coefficients are simply coefficients of γ with respect to the wavelet basis. Is a signal with a discrete time, that is a 2L-dimensional real vector from V. Technical Note: Using wavelet analyses on water depth time series to detect glacial influence in high-mountain hydrosystems. They could be efficiently evaluated by passing γ through a series of filters (linear operators) obtaining at each step: i) wavelet coefficients for a given level, and ii) a downsampled signal to which the next round of evaluation is to be applied: