WaveletComp: an R package for computational wavelet analysis
WaveletComp is an R package for continuous wavelet-based analysis of univariate and bivariate time series. Wavelet functions are implemented in WaveletComp such that a wide range of intermediate and final results are easily accessible. The null hypothesis that there is no (joint) periodicity in the series is tested via p-values obtained from simulation, where the model to be simulated can be chosen from a wide variety of options.
The reconstruction, and thus filtering, of a given series from its wavelet decomposition, subject to a range of possible constraints, is also possible.
WaveletComp provides extended plotting functionality – which objects should be added to a plot (for example, the ridge of wavelet power, contour lines indicating significant periodicity, arrows indicating the leading/lagging series), which kind and degree of smoothing is desired in wavelet coherence plots, which color palette to use, how to define the layout of the time axis (using POSIXt conventions), and others.
Technically, we have developed vector- and matrix-based implementations of algorithms to reduce computation time.
Easy and intuitive handling is given high priority in WaveletComp.
Keywords: R project; wavelet analysis; Morlet wavelet; (cross-) wavelet power; wavelet coherence; wavelet reconstruction; wavelet graphics; wavelet filtering
A simple example from our guide book
Plot of a time series with period getting longer:
Power spectrum of this series, made with WaveletComp:
The increasing period is represented by the black line (the ridge of the power spectrum).
The following plot shows a reconstruction of the original series, retaining only significant periods. This is data reduction! Only important parts are retained. (What is important depends on the application, of course.)