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 hotmail.com> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| CRAN checks: |
mlmts results |