TSdist (version 3.1)

TSdist-package: Distance Measures for Time Series in R.

Description

A complete set of distance measures specifically designed to deal with time series.

Details

ll{ Package: TSdist Type: Package Version: 3.1 Date: 2015-07-14 License: GPL (>=2) } This package provides a comprehensive set of time series distance measures published in the literature and some additional functions which, although initially not designed for this purpose, can be used to measure the dissimilarity between time series. These measures can be used to perform clustering, classification or other data mining tasks which require the definition of a distance measure between time series. Some of the measures are specifically implemented for this package while other are originally hosted in other R packages. The measures included are:

• Lp distancesLPDistance
• Distance based on the cross-correlationCCorDistance
• Short Time Series distance (STS)STSDistance
• Dynamic Time Warping (DTW)DTWDistance
• LB_Keogh lower bound for the Dynamic Time Warping distanceLBKeoghDistance
• Edit Distance for Real Sequences (EDR)EDRDistance
• Longest Common Subsequence distance for real sequences(LCSS)LCSSDistance
• Edit Distance based on Real Penalty (ERP)ERPDistance
• Distance based on the Fourier Discrete TransformFourierDistance
• TQuest distanceTquestDistance
• Dissim distanceDissimDistance
• Autocorrelation-based dissimilarityACFDistance.
• Partial autocorrelation-based dissimilarityPACFDistance.
• Dissimilarity based on LPC cepstral coefficientsARLPCCepsDistance.
• Model-based dissimilarity proposed by Maharaj (1996, 2000)ARMahDistance.
• Model-based dissimilarity proposed by Piccolo (1990)ARPicDistance.
• Compression-based dissimilarity measureCDMDistance.
• Complexity-invariant distance measureCIDDistance.
• Dissimilarities based on Pearson's correlationCorDistance.
• Dissimilarity index which combines temporal correlation and raw value behaviorsCortDistance.
• Dissimilarity based on wavelet feature extractionWavDistance.
• Integrated periodogram based dissimilarityIntPerDistance.
• Periodogram based dissimilarityPerDistance.
• Symbolic Aggregate Aproximation based dissimilarityMindistSaxDistance.
• Normalized compression based distanceNCDDistance.
• Dissimilarity measure cased on nonparametric forecastsPredDistance.
• Dissimilarity based on the integrated squared difference between the log-spectraSpecISDDistance.
• General spectral dissimilarity measure using local-linear estimation of the log-spectraSpecLLRDistance.
• Permutation Distribution DistancePDCDistance.
• Frechet distanceFrechetDistance.

All the measures are implemented in separate functions but can also be invoked by means of the wrapper function TSDistances. Moreover, this distance enables the use of time series objects of type ts, zoo and xts.

As an additional functionality of the package, pairwise distances between all the time series in a database can be easily computed by using the dist function from the proxy package or the TSDatabaseDistances function included in the TSdist package.

References

Esling, P., & Agon, C. (2012). Time-series data mining. ACM Computing Surveys, 45(1), 1-34.

Liao, T. W. (2005). Clustering of time series data-a survey. Pattern Recognition, 38(11), 1857-1874.

Wang, X., Mueen, A., Ding, H., Trajcevski, G., Scheuermann, P., & Keogh, E. (2012). Experimental comparison of representation methods and distance measures for time series data. Data Mining and Knowledge Discovery, 26(2), 275-309.

David Meyer and Christian Buchta (2013). proxy: Distance and Similarity Measures. R package version 0.4-10. http://CRAN.R-project.org/package=proxy

Examples

library(TSdist);