Implements feature selection routines for multivariate time series (MTS). The list of implemented algorithms includes: own lags (independent MTS components), distance-based (using external structure, e.g. Pfeifer and Deutsch (1980) <doi:10.2307/1268381>), cross-correlation (see Schelter et al. (2006, ISBN:9783527406234)), graphical LASSO (see Haworth and Cheng (2014) <https://www.gla.ac.uk/media/Media_401739_smxx.pdf>), random forest (see Pavlyuk (2020) "Random Forest Variable Selection for Sparse Vector Autoregressive Models" in Contributions to Statistics, in production), least angle regression (see Gelper and Croux (2008) <https://lirias.kuleuven.be/retrieve/16024>), mutual information (see Schelter et al. (2006, ISBN:9783527406234), Liu et al. (2016) <doi:10.1109/ChiCC.2016.7554480>), and partial spectral coherence (see Davis et al.(2016) <doi:10.1080/10618600.2015.1092978>). In addition, the package implements functions for ensemble feature selection (using feature ranking and majority voting). The package is implemented within Dmitry Pavlyuk's research project No. 1.1.1.2/VIAA/1/16/112 "Spatiotemporal urban traffic modelling using big data".
Version: | 0.1.7 |
Depends: | R (≥ 3.6) |
Imports: | glasso, lars, mpmi, freqdom, randomForestSRC |
Suggests: | knitr, rmarkdown, sparsevar, plot.matrix, svMisc, MTS |
Published: | 2022-04-26 |
Author: | Dmitry Pavlyuk [aut, cre] |
Maintainer: | Dmitry Pavlyuk <Dmitry.Pavlyuk at tsi.lv> |
License: | GPL-3 |
NeedsCompilation: | no |
Materials: | NEWS |
In views: | TimeSeries |
CRAN checks: | fsMTS results |
Reference manual: | fsMTS.pdf |
Vignettes: |
Feature selection for a simulated data set Feature selection for a real traffic data set |
Package source: | fsMTS_0.1.7.tar.gz |
Windows binaries: | r-devel: fsMTS_0.1.7.zip, r-release: fsMTS_0.1.7.zip, r-oldrel: fsMTS_0.1.7.zip |
macOS binaries: | r-release (arm64): fsMTS_0.1.7.tgz, r-oldrel (arm64): not available, r-release (x86_64): fsMTS_0.1.7.tgz, r-oldrel (x86_64): fsMTS_0.1.7.tgz |
Old sources: | fsMTS archive |
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