CRAN/E | fsMTS

fsMTS

Feature Selection for Multivariate Time Series

Installation

About

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) ), 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) 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".

Key Metrics

Version 0.1.7
R ≥ 3.6
Published 2022-04-26 703 days ago
Needs compilation? no
License GPL-3
CRAN checks fsMTS results

Downloads

Yesterday 6 0%
Last 7 days 15 -61%
Last 30 days 203 -25%
Last 90 days 844 +6%
Last 365 days 3.193 -3%

Maintainer

Maintainer

Dmitry Pavlyuk

Dmitry.Pavlyuk@tsi.lv

Authors

Dmitry Pavlyuk

aut / cre

Material

NEWS
Reference manual
Package source

In Views

TimeSeries

Vignettes

Feature selection for a simulated data set
Feature selection for a real traffic data set

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

fsMTS archive

Depends

R ≥ 3.6

Imports

glasso
lars
mpmi
freqdom
randomForestSRC

Suggests

knitr
rmarkdown
sparsevar
plot.matrix
svMisc
MTS