DTWDistSakoeChiba_fast: (Fast) Dynamic Time Warping Dissimilarity with a Sakoe-Chiba...

Description Usage Arguments Details Value References See Also

Description

Fast version of univariate dynamic time warping (Sakoe-Chiba window as constraint, symmetric1 step pattern) which uses a cyclic access strategy with a smaller cost matrix; inspired by the C implementation of dynamic time warping in dtwclust::dtw_basic(), but cuts even more overhead.

Usage

1
DTWDistSakoeChiba_fast(x, y, windowSize)

Arguments

x

1st numeric vector/time series.

y

2nd numeric vector/time series.

windowSize

The maximum index difference which is considered when matching elements. If greater/equal one, interpreted as absolute value. If smaller then one, interpreted as fraction of the length of the longer time series.

Details

Be aware that it is not really a distance in the strict sense, as DTW violates the triangle inequality.

Value

The distance as double (not-a-number if matching is not possible as the time series lengths differ more than windowSize).

References

Berndt, D. J. & Clifford, J. (1994). Using dynamic time warping to find patterns in time series. In Proceedings of the 3rd international conference on knowledge discovery and data mining (pp. 359–370). AAAI Press.

Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE transactions on acoustics, speech, and signal processing, 26(1), 43-49.

See Also

Other DTW functions: DTWDistMult_fast, DTWDistSakoeChibaMult_fast, DTWDist_fast


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