Distance measures for time series in r: The TSdist package

63Citations
Citations of this article
763Readers
Mendeley users who have this article in their library.

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.

References Powered by Scopus

Clustering of time series data - A survey

1997Citations
N/AReaders
Get full text

A review on time series data mining

1246Citations
N/AReaders
Get full text

Computing and visualizing dynamic time warping alignments in R: The dtw package

865Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Clustering-based anomaly detection in multivariate time series data

163Citations
N/AReaders
Get full text

Time-series clustering in R Using the dtwclust package

99Citations
N/AReaders
Get full text

Social Media's Impact on the Consumer Mindset: When to Use Which Sentiment Extraction Tool?

68Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Mori, U., Mendiburu, A., & Lozano, J. A. (2016). Distance measures for time series in r: The TSdist package. R Journal, 8(2), 455–463. https://doi.org/10.32614/rj-2016-058

Readers over time

‘14‘15‘16‘17‘18‘19‘20‘21‘22‘23‘24‘250306090120

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 311

71%

Researcher 76

17%

Professor / Associate Prof. 39

9%

Lecturer / Post doc 13

3%

Readers' Discipline

Tooltip

Agricultural and Biological Sciences 90

37%

Medicine and Dentistry 63

26%

Computer Science 48

20%

Engineering 45

18%

Save time finding and organizing research with Mendeley

Sign up for free
0