TSclust (version 1.1)

diss.DWT: Dissimilarity for Time Series Based on Wavelet Feature Extraction

Description

Performs an unsupervised feature extration using orthogonal wavelets on the series and returns the Euclidean distance between the wavelet approximations in an appropriate scale.

Usage

diss.DWT(series)

Arguments

series

Numeric matrix with row order time series

Value

• Returns an object of type dist with the pairwise distances.

Details

The set of original series is replaced by their wavelet approximation coefficients in an appropriate scale, and the dissimilarity between two series is computed as the Euclidean distance between these coefficients. The appropriate scale is automatically determined by using an algorithm addressed to obtain an efficient reduction of the dimensionality but preserving as much information from the original data as possible. The algorithm is introduced by Zhang, Ho, Zhang, and Lin (2006).

References

Zhang, H., Ho, T. B., Zhang, Y., and Lin, M. (2006) Unsupervised feature extraction for time series clustering using orthogonal wavelet transform. INFORMATICA-LJUBLJANA-, 30(3), 305.

Examples

## Create three sample time series
x <- cumsum(rnorm(100))
y <- cumsum(rnorm(100))
z <- sin(seq(0, pi, length.out=100))

#compute the distance
diss.DWT(rbind(x, y, z))