The definition of a distance measure between time series is crucial for many time series data mining tasks, such as clustering and classification. For this reason, a vast portfolio of time series distance measures has been published in the past few years. In this paper, the TSdist package is presented, a complete tool which provides a unified framework to calculate the largest variety of time series dissimilarity measures available in R at the moment, to the best of our knowledge. The package implements some popular distance measures which were not previously available in R, and moreover, it also provides wrappers for measures already included in other R packages. Additionally, the application of these distance measures to clustering and classification tasks is also supported in TSdist, directly enabling the evaluation and comparison of their performance within these two frameworks.
TSdist, dtw, pdc, proxy, longitudinalData, TSclust, zoo, xts
TimeSeries, Econometrics, Finance, Environmetrics, Multivariate, SpatioTemporal
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For attribution, please cite this work as
Mori, et al., "The R Journal: Distance Measures for Time Series in R: The TSdist Package", The R Journal, 2016
BibTeX citation
@article{RJ-2016-058,
author = {Mori, Usue and Mendiburu, Alexander and Lozano, Jose A.},
title = {The R Journal: Distance Measures for Time Series in R: The TSdist Package},
journal = {The R Journal},
year = {2016},
note = {https://doi.org/10.32614/RJ-2016-058},
doi = {10.32614/RJ-2016-058},
volume = {8},
issue = {2},
issn = {2073-4859},
pages = {451-459}
}