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fsMTS
View on CRAN: Click
here
Download and install fsMTS package within the R console
Install from CRAN:
install.packages("fsMTS")
Install from Github:
library("remotes")
install_github("cran/fsMTS")
Install by package version:
library("remotes")
install_version("fsMTS", "0.1.7")
Attach the package and use:
library("fsMTS")
Maintained by
Dmitry Pavlyuk
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All associated links for this package
First Published: 2020-03-25
Latest Update: 2022-04-26
Description:
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) ),
cross-correlation (see Schelter et al. (2006, ISBN:9783527406234)),
graphical LASSO (see Haworth and Cheng (2014) ),
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) ),
mutual information (see Schelter et al. (2006, ISBN:9783527406234), Liu et al. (2016) ),
and partial spectral coherence (see Davis et al.(2016) ).
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".
How to cite:
Dmitry Pavlyuk (2020). fsMTS: Feature Selection for Multivariate Time Series. R package version 0.1.7, https://cran.r-project.org/web/packages/fsMTS. Accessed 21 Dec. 2024.
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Functions, R codes and Examples using
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Some associated functions: cutoff . fsEnsemble . fsMTS-package . fsMTS . fsSimilarity . fsSimilarityMatrix . fsSparsity . traffic.mini . traffic .
Some associated R codes: fsCCF.R . fsDistance.R . fsGLASSO.R . fsIndependent.R . fsLARS.R . fsMI.R . fsMTS-package.R . fsMTS.R . fsPSC.R . fsRF.R . utils.R . Full fsMTS package functions and examples
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