IncDTW: An R Package for Incremental Calculation of Dynamic Time Warping

IncDTW: An R Package for Incremental Calculation of Dynamic Time Warping

: dynamic time warping, time series, k-NN, subsequence matching, distance measure,
clustering, classification

Dynamic time warping (DTW) is a popular distance measure for time series analysis
and has been applied in many research domains. This paper proposes the R package
IncDTW for the incremental calculation of DTW, and based on this principle IncDTW
also helps to classify or cluster time series, or perform subsequence matching and k-nearest
neighbor search. DTW can measure dissimilarity between two temporal sequences which
may vary in speed, with a major downside of high computational costs. Especially for
analyzing live data streams, subsequence matching or calculating pairwise distance matrices, runtime intensive computations are unfavorable or can even make the analysis
intractable. IncDTW tackles this problem by a vector-based implementation of the DTW
algorithm to reduce the space complexity from a quadratic to a linear level in number
of observations, and an incremental calculation of DTW for updating interim results to
reduce the runtime complexity for online applications.
We discuss the fundamental functionalities of IncDTW and apply the package to
classify multivariate live stream accelerometer time series for activity recognition. Finally,
comparative runtime experiments with various R and Python packages for various data
analysis tasks emphasize the broad applicability of IncDTW.

Maximilian Leodolter

https://www.jstatsoft.org/article/view/v099i09

Austrian Institute
of Technology

August 2021

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