R: Distance Measures for Time Series in R.

TSdist-package {TSdist} | R Documentation

## Distance Measures for Time Series in R.

### Description

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

### Details

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 distances `LPDistance`

- Distance based on the cross-correlation `CCorDistance`

- Short Time Series distance (STS) `STSDistance`

- Dynamic Time Warping (DTW) `DTWDistance`

- LB_Keogh lower bound for the Dynamic Time Warping distance `LBKeoghDistance`

- 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 Transform `FourierDistance`

- TQuest distance `TquestDistance`

- Dissim distance `DissimDistance`

- Autocorrelation-based dissimilarity `ACFDistance`.

- Partial autocorrelation-based dissimilarity `PACFDistance`.

- Dissimilarity based on LPC cepstral coefficients `ARLPCCepsDistance`.

- Model-based dissimilarity proposed by Maharaj (1996, 2000) `ARMahDistance`.

- Model-based dissimilarity proposed by Piccolo (1990) `ARPicDistance`.

- Compression-based dissimilarity measure `CDMDistance`.

- Complexity-invariant distance measure `CIDDistance`.

- Dissimilarities based on Pearson's correlation `CorDistance`.

- Dissimilarity index which combines temporal correlation and raw value behaviors `CortDistance`.

- Dissimilarity based on wavelet feature extraction `WavDistance`.

- Integrated periodogram based dissimilarity `IntPerDistance`.

- Periodogram based dissimilarity `PerDistance`.

- Symbolic Aggregate Aproximation based dissimilarity `MindistSaxDistance`.

- Normalized compression based distance `NCDDistance`.

- Dissimilarity measure cased on nonparametric forecasts `PredDistance`.

- Dissimilarity based on the integrated squared difference between the log-spectra `SpecISDDistance`.

- General spectral dissimilarity measure using local-linear estimation of the log-spectra `SpecLLRDistance`.

- Permutation Distribution Distance `PDCDistance`.

- Frechet distance `FrechetDistance`.

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.

### Author(s)

Usue Mori, Alexander Mendiburu, Jose A. Lozano. Maintainer: <usue.mori@ehu.es>

### 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);

[Package TSdist version 3.6 Index]