Package: TSclust 1.3.1

TSclust: Time Series Clustering Utilities

A set of measures of dissimilarity between time series to perform time series clustering. Metrics based on raw data, on generating models and on the forecast behavior are implemented. Some additional utilities related to time series clustering are also provided, such as clustering algorithms and cluster evaluation metrics.

Authors:Pablo Montero Manso [cre], Jose Vilar Fernandez [aut]

TSclust_1.3.1.tar.gz
TSclust_1.3.1.zip(r-4.5)TSclust_1.3.1.zip(r-4.4)TSclust_1.3.1.zip(r-4.3)
TSclust_1.3.1.tgz(r-4.4-any)TSclust_1.3.1.tgz(r-4.3-any)
TSclust_1.3.1.tar.gz(r-4.5-noble)TSclust_1.3.1.tar.gz(r-4.4-noble)
TSclust_1.3.1.tgz(r-4.4-emscripten)TSclust_1.3.1.tgz(r-4.3-emscripten)
TSclust.pdf |TSclust.html✨
TSclust/json (API)

# Install 'TSclust' in R:
install.packages('TSclust', repos = c('https://pmontman.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/pmontman/tsclust/issues

Datasets:

On CRAN:

5.75 score 2 stars 8 packages 168 scripts 793 downloads 7 mentions 30 exports 77 dependencies

Last updated 4 years agofrom:8a0601e343. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 27 2024
R-4.5-winNOTEOct 27 2024
R-4.5-linuxNOTEOct 27 2024
R-4.4-winNOTEOct 27 2024
R-4.4-macNOTEOct 27 2024
R-4.3-winNOTEOct 27 2024
R-4.3-macNOTEOct 27 2024

Exports:cluster.evaluationconvert.to.SAX.symboldissdiss.ACFdiss.AR.LPC.CEPSdiss.AR.MAHdiss.AR.PICdiss.CDMdiss.CIDdiss.CORdiss.CORTdiss.DTWARPdiss.DWTdiss.EUCLdiss.FRECHETdiss.INT.PERdiss.MINDIST.SAXdiss.NCDdiss.PACFdiss.PDCdiss.PERdiss.PREDdiss.SPEC.GLKdiss.SPEC.ISDdiss.SPEC.LLRloo1nn.cvMINDIST.SAXPAApvalues.clustSAX.plot

Dependencies:base64encbslibcachemclasscliclusterclvcolorspacecurldigestdtwevaluatefansifarverfastmapfontawesomeforecastfracdifffsgenericsggplot2gluegtablehighrhtmltoolshtmlwidgetsisobandjquerylibjsonliteKernSmoothknitrlabelinglatticelifecyclelmtestlocpollongitudinalDatamagrittrMASSMatrixmemoisemgcvmimemisc3dmunsellnlmennetpdcpillarpkgconfigproxyquadprogquantmodR6rappdirsRColorBrewerRcppRcppArmadillorglrlangrmarkdownsassscalestibbletimeDatetinytextseriesTTRurcautf8vctrsviridisLitewithrxfunxtsyamlzoo

Readme and manuals

Help Manual

Help pageTopics
Clustering Evaluation Index Based on Known Ground Truthcluster.evaluation
TSclust Dissimilarity Computationdiss
Autocorrelation-based Dissimilaritydiss.ACF diss.PACF
Dissimilarity Based on LPC Cepstral Coefficientsdiss.AR.LPC.CEPS
Model-based Dissimilarity Proposed by Maharaj (1996, 2000)diss.AR.MAH
Model-based Dissimilarity Measure Proposed by Piccolo (1990)diss.AR.PIC
Compression-based Dissimilarity measurediss.CDM
Complexity-Invariant Distance Measure For Time Seriesdiss.CID
Correlation-based Dissimilaritydiss.COR
Dissimilarity Index Combining Temporal Correlation and Raw Values Behaviorsdiss.CORT
Dynamic Time Warping Distancediss.DTWARP
Dissimilarity for Time Series Based on Wavelet Feature Extractiondiss.DWT
Euclidean Distancediss.EUCL
Frechet Distancediss.FRECHET
Integrated Periodogram Based Dissimilaritydiss.INT.PER
Symbolic Aggregate Aproximation related functionsconvert.to.SAX.symbol diss.MINDIST.SAX diss.SAX MINDIST.SAX PAA SAX.plot
Normalized Compression Distancediss.NCD
Permutation Distribution Distancediss.PDC
Periodogram Based Dissimilaritydiss.PER
Dissimilarity Measure Based on Nonparametric Forecastdiss.PRED
Dissimilarity based on the Generalized Likelihood Ratio Testdiss.SPEC.GLK
Dissimilarity Based on the Integrated Squared Difference between the Log-Spectradiss.SPEC.ISD
General Spectral Dissimilarity Measure Using Local-Linear Estimation of the Log-Spectradiss.SPEC.LLR
Hourly Electricity Prices in the Spanish Marketelectricity
Long-Term Interest Rates from 1995 to 2012interest.rates
Clustering Evaluation Index Based on Leave-one-out One-nearest-neighbor Evaluationloo1nn.cv
Pairs of Time Series from Different Domainspaired.tseries
Clustering Algorithm Based on p-values.pvalues.clust
Synthetic Time Series for Clustering Performace Comparisons.synthetic.tseries
Package for Time Series Clustering.TSclust-package TSclust