l2DistMult_fast: (Fast) Multi-variate L2 Distance

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

Description

Computes the standard Euclidean distance between multi-variate time series (according to Kotsakos, Trajcevski, Gunopulos and Aggarwal (2014)) with a fast C++ implementation.

Usage

l2DistMult_fast(x, y, cid = FALSE, cortK = -1)

Arguments

x	1st numeric matrix/multi-variate time series.
y	2nd numeric matrix/multi-variate time series.
cid	Should the distance be made complexity invariant (l2CompCorFactorMult_fast)?
cortK	Should the temporal behavior (correlation) of the time series' diff vectors be considered (cortFactorMult_fast)? A factor smaller than 0 means no, higher factors will be used as parameter k in the temporal correlation scaling function.

Value

The distance as double.

References

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

Other L_p distances: l1DistMult_fast, l1Dist_fast, l2Dist_fast, l2Dist, l2Norm_fast, lmaxDistMult_fast, lmaxDist_fast

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