fsMTS-package

<p>Feature selection for Multivariate Time Series</p>

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".

fsMTS-package: Feature selection for Multivariate Time Series

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