Package smile.wavelet
Interface WaveletShrinkage
public interface WaveletShrinkage
The wavelet shrinkage is a signal denoising technique based on the idea of
thresholding the wavelet coefficients. Wavelet coefficients having small
absolute value are considered to encode mostly noise and very fine details
of the signal. In contrast, the important information is encoded by the
coefficients having large absolute value. Removing the small absolute value
coefficients and then reconstructing the signal should produce signal with
lesser amount of noise. The wavelet shrinkage approach can be summarized as
follows:
 Apply the wavelet transform to the signal.
 Estimate a threshold value.
 The socalled hard thresholding method zeros the coefficients that are smaller than the threshold and leaves the other ones unchanged. In contrast, the soft thresholding scales the remaining coefficients in order to form a continuous distribution of the coefficients centered on zero.
 Reconstruct the signal (apply the inverse wavelet transform).

Method Summary

Method Details

denoise
Adaptive hardthresholding denoising a time series with given wavelet. Parameters:
t
 the time series array. The size should be a power of 2. For time series of size no power of 2, 0 padding can be applied.wavelet
 the wavelet to transform the time series.

denoise
Adaptive denoising a time series with given wavelet. Parameters:
t
 the time series array. The size should be a power of 2. For time series of size no power of 2, 0 padding can be applied.wavelet
 the wavelet to transform the time series.soft
 true if apply soft thresholding.
