pdcDistTwoTSMult: Multi-variate Permutation Distribution Distance for Two Time...

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

Description

Multi-variate version of pdcDistTwoTS which calculates the pdc distance for each attribute and then takes the l2 norm of the resulting vector as proposed by Brandmaier (2011, p. 18).

Usage

pdcDistTwoTSMult(x, y, subSequenceLength = NULL)

Arguments

x	1st numeric matrix (multi-variate time series; minimum number of rows: 2).
y	2nd numeric matrix (multi-variate time series; minimum number of rows: 2; needs to have the same number of columns as the first time series).
subSequenceLength	Number of elements which form each subsequence. Will be determined as integer in [2,7] by a heuristic if not provided.

Value

The distance as double from the range [0,4*attributeCount].

References

Brandmaier, A. M. (2011). Permutation distribution clustering and structural equation model trees (Doctoral dissertation, Universität des Saarlandes, Saarbrücken).

Brandmaier, A. M. (2015). pdc: An R Package for Complexity-Based Clustering of Time Series. Journal of Statistical Software, 67(5), 1-23.

See Also

Other PDC functions: pdcDistTSListMult, pdcDistTSList, pdcDistTwoTS, pdcEntropyHeuristicMult, pdcEntropyHeuristic, pdcEntropy

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