The R Journal: article published in 2016, volume 8:2

Distance Measures for Time Series in R: The TSdist Package PDF download
Usue Mori, Alexander Mendiburu and Jose A. Lozano , The R Journal (2016) 8:2, pages 451-459.

Abstract 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.

Received: 2016-05-29; online 2016-09-09
CRAN packages: TSdist, dtw, pdc, proxy, longitudinalData, TSclust, zoo, xts , CRAN Task Views implied by cited CRAN packages: TimeSeries, Econometrics, Finance, Environmetrics, Multivariate, SpatioTemporal


CC BY 4.0
This article is licensed under a Creative Commons Attribution 3.0 Unported license .

@article{RJ-2016-058,
  author = {Usue Mori and Alexander Mendiburu and Jose A. Lozano},
  title = {{Distance Measures for Time Series in R: The TSdist Package}},
  year = {2016},
  journal = {{The R Journal}},
  url = {https://journal.r-project.org/archive/2016/RJ-2016-058/index.html},
  pages = {451--459},
  volume = {8},
  number = {2}
}