mlmts: Machine Learning Algorithms for Multivariate Time Series

An implementation of several machine learning algorithms for multivariate time series. The package includes functions allowing the execution of clustering, classification or outlier detection methods, among others. It also incorporates a collection of multivariate time series datasets which can be used to analyse the performance of new proposed algorithms. Practitioners from a broad variety of fields could benefit from the general framework provided by 'mlmts'.

Version: 1.0.1
Depends: R (≥ 4.0.0)
Imports: quantspec, waveslim, Rfast, TSclust, forecast, tseries, TSA, tsfeatures, tseriesChaos, freqdom, e1071, dtw, base, psych, complexplus, MTS, Matrix, ggplot2, multiwave, MASS, fda.usc, TSdist, evolqg, geigen, DescTools, pracma, pspline, Rdpack, stats, ClusterR, AID, caret, ranger
Suggests: testthat (≥ 3.0.0)
Published: 2022-04-19
Author: Angel Lopez-Oriona [aut, cre], Jose A. Vilar [aut]
Maintainer: Angel Lopez-Oriona <oriona38 at>
License: GPL-2
NeedsCompilation: no
CRAN checks: mlmts results


Reference manual: mlmts.pdf


Package source: mlmts_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mlmts_1.0.1.tgz, r-oldrel (arm64): mlmts_1.0.1.tgz, r-release (x86_64): mlmts_1.0.1.tgz, r-oldrel (x86_64): mlmts_1.0.1.tgz
Old sources: mlmts archive


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