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

The wavelet-based tools for analysis of time series are important because they have been shown to provide a better estimator (and confidence intervals) than other approaches for the Hurst parameter [14]. Is a signal with a discrete time, that is a 2L-dimensional real vector from V. When applied to time-series data, wavelet analysis involves a transform from the given one-dimensional time series to a two-dimensional time-frequency image. Its wavelet coefficients are simply coefficients of γ with respect to the wavelet basis. Details of scaling and translation of the Morlet wavelet with an interactive Demonstration. 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: