cortFactorMult_fast: Multi-variate Temporal Correlation-Based Correction Factor

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

Description

Considers the dissimilarity of two time series regarding their behavior, namely if they move in the same direction (diff vectors used) at the different points in time. This correlation between [-1,1] is firstly calculated for each dimension/attribute separately and then averaged. It is scaled with the exponential function to (0,2) (depending on k) and should be multiplied with another dissimilarity to enhance it with this behavioral information. Introduced by Chouakria and Nagabhushan (2007) for the uni-variate case.

Usage

cortFactorMult_fast(x, y, k = 2)

Arguments

x	1st numeric matrix/multi-variate time series.
y	2nd numeric matrix/multi-variate time series.
k	A non-negative constant for scaling.

Details

This factor is currently integrated as a parameter into the L2 distance and dynamic time warping distance of this package.

Value

The correlation-based scaling factor as double from the range (0,2).

References

Chouakria, A. D. & Nagabhushan, P. N. (2007). Adaptive dissimilarity index for measuring time series proximity. Advances in Data Analysis and Classification, 1(1), 5-21.

Kotsakos, D., Trajcevski, G., Gunopulos, D. & Aggarwal, C. C. (2014). Time-series data clustering. In C. C. Aggarwal & C. K. Reddy (Eds.), Data clustering : Algorithms and applications (pp. 357–380). Chapman & Hall/CRC data mining and knowledge discovery series. Boca Raton: CRC Press.

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