Description Usage Arguments Details Value References See Also
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.
1 | DTWDistSakoeChiba_fast(x, y, windowSize)
|
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. |
Be aware that it is not really a distance in the strict sense, as DTW violates the triangle inequality.
The distance as double (not-a-number if matching is not possible
as the time series lengths differ more than windowSize
).
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.
Other DTW functions: DTWDistMult_fast
,
DTWDistSakoeChibaMult_fast
,
DTWDist_fast
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