l2CompCorFactorMult_fast: Multi-variate L2 Complexity Correction Factor

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

Description

Calculates the complexity correction factor for the distance between two multi-variate time series, using the L2 norm of each time series' attributes' diff vectors as complexity estimate. Can be combined with any distance as a scaling factor (distances between vectors of different complexity become more prominent). Does not obey the triangular equality if its combined with the Euclidean distance, but a relaxed version (see reference).

Usage

l2CompCorFactorMult_fast(x, y)

Arguments

x	1st numeric matrix/multi-variate time series.
y	2nd numeric vector/multi-variate time series.

Details

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

Value

The complexity correction factor as double. Is infinity if one series is constant in all attributes and the other one not.

References

Batista, G. E., Keogh, E. J., Tataw, O. M. & De Souza, V. M. (2014). Cid: An efficient complexity-invariant distance for time series. Data Mining and Knowledge Discovery, 28(3), 634-669.

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.

See Also

l2Dist_fast, DTWDist_fast

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