corDist_fast: (Fast) Correlation-based Dissimilarity

In Jakob-Bach/FastTSDistances: Fast dissimilarity computations for time series

Description

Computes correlation-based dissimilarity as described by Golay et al. (1998). The coding is inspired by the TSclust::diss.cor() method, but faster because of the the C++ implementation.

Usage

corDist_fast(x, y, beta = 0)

Arguments

x	1st numeric vector/time series.
y	2nd numeric vector/time series.
beta	If this parameter is smaller/equal zero, the formula dist(x,y) = sqrt(2*(1-cor(x,y))) is used (d1 in the paper, equals l2Dist(x,y)/sqrt(n) if time series are z-standardized), otherwise dist(x,y) = sqrt(((1-cor(x,y))/(1+cor(x,y)))^beta) (called d2 in the paper).

Value

The dissimilarity as double in the range [0,sqrt(2)] if beta == 0 and [0,Inf] otherwise. Is NaN if at least one series is constant.

References

Golay, X., Kollias, S., Stoll, G., Meier, D., Valavanis, A. & Boesiger, P. (1998). A new correlation-based fuzzy logic clustering algorithm for fmri. Magnetic Resonance in Medicine, 40(2), 249-260.

Jakob-Bach/FastTSDistances documentation built on May 13, 2019, 1:15 p.m.