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Discrete wavelet transform (DWT).
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def
dwt(t: Array[Double], filter: String): Unit
Discrete wavelet transform.
Discrete wavelet transform.
 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.
 filter
wavelet filter.
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def
idwt(wt: Array[Double], filter: String): Unit
Inverse discrete wavelet transform.
Inverse discrete wavelet transform.
 wt
the wavelet coefficients. The size should be a power of 2. For time series of size no power of 2, 0 padding can be applied.
 filter
wavelet filter.

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def
wavelet(filter: String): Wavelet
Returns the wavelet filter.
Returns the wavelet filter. The filter name is derived from one of four classes of wavelet transform filters: Daubechies, Least Asymetric, Best Localized and Coiflet. The prefixes for filters of these classes are d, la, bl and c, respectively. Following the prefix, the filter name consists of an integer indicating length. Supported lengths are as follows:
Daubechies 4,6,8,10,12,14,16,18,20.
Least Asymetric 8,10,12,14,16,18,20.
Best Localized 14,18,20.
Coiflet 6,12,18,24,30.
Additionally "haar" is supported for Haar wavelet.
Besides, "d4", the simplest and most localized wavelet, uses a different centering method from other Daubechies wavelet.
 filter
filter name

def
wsdenoise(t: Array[Double], filter: String, soft: Boolean = false): Unit
The wavelet shrinkage is a signal denoising technique based on the idea of thresholding the wavelet coefficients.
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).
The biggest challenge in the wavelet shrinkage approach is finding an appropriate threshold value. In this method, we use the universal threshold T = σ sqrt(2*log(N)), where N is the length of time series and σ is the estimate of standard deviation of the noise by the socalled scaled median absolute deviation (MAD) computed from the highpass wavelet coefficients of the first level of the transform.
 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.
 filter
the wavelet filter to transform the time series.
 soft
true if apply soft thresholding.
 def →[B](y: B): (Operators, B)
High level Smile operators in Scala.