EDRDistMult_fast: (Fast) Multi-variate Edit Distance on Real Sequence

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

Description

Computes the Edit distance on Real Sequence as described by Chen, Özsu and Oria (2005). A match between two time series elements exists if the L1 distances between corresponding attributes are all is below an epsilon. Apart from that, the computation is similar to the standard edit distance. The coding is inspired by the TSdist::EDRDistance() method, but faster because point-to-point distances computation is integrated into the C++ code.

Usage

EDRDistMult_fast(x, y, epsilon, normalize = FALSE)

Arguments

x	1st numeric matrix/multi-variate time series.
y	2nd numeric matrix/multi-variate time series.
epsilon	Maximum distance between two time series elements to count a match.
normalize	Normalize the result to [0,1] considering the maximum possible dissimilarity.

Details

Despite the name, it is not really a distance in the strict sense, as EDR violates the triangular inequality.

Value

The distance as double.

References

Chen, L., Özsu, M. T. & Oria, V. (2005). Robust and fast similarity search for moving object trajectories. In Proceedings of the 2005 acm sigmod international conference on management of data (pp. 491–502). ACM.

See Also

Other Edit distance functions: EDRDistSakoeChibaMult_fast, EDRDistSakoeChiba_fast, EDRDist_fast, ERPDistMult_fast, ERPDistSakoeChibaMult_fast, ERPDistSakoeChiba_fast, ERPDistSakoeChiba, ERPDist_fast, ERPDist

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