TSrepr: Time Series Representations

Methods for representations (i.e. dimensionality reduction, preprocessing, feature extraction) of time series to help more accurate and effective time series data mining. Non-data adaptive, data adaptive, model-based and data dictated (clipped) representation methods are implemented. Also various normalisation methods (min-max, z-score, Box-Cox, Yeo-Johnson), and forecasting accuracy measures are implemented.

Version: 1.1.0
Depends: R (≥ 2.10)
Imports: Rcpp (≥ 0.12.12), MASS, quantreg, wavelets, mgcv, dtt
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, ggplot2, data.table, moments, testthat
Published: 2020-07-13
Author: Peter Laurinec ORCID iD [aut, cre]
Maintainer: Peter Laurinec <tsreprpackage at gmail.com>
BugReports: https://github.com/PetoLau/TSrepr/issues
License: GPL-3 | file LICENSE
URL: https://petolau.github.io/package/, https://github.com/PetoLau/TSrepr/
NeedsCompilation: yes
Citation: TSrepr citation info
Materials: NEWS
In views: TimeSeries
CRAN checks: TSrepr results

Downloads:

Reference manual: TSrepr.pdf
Vignettes: Extending TSrepr
Time series representations in R
Use case: clustering time series representations
Package source: TSrepr_1.1.0.tar.gz
Windows binaries: r-devel: TSrepr_1.1.0.zip, r-devel-UCRT: TSrepr_1.1.0.zip, r-release: TSrepr_1.1.0.zip, r-oldrel: TSrepr_1.1.0.zip
macOS binaries: r-release (arm64): TSrepr_1.1.0.tgz, r-release (x86_64): TSrepr_1.1.0.tgz, r-oldrel: TSrepr_1.1.0.tgz
Old sources: TSrepr archive

Reverse dependencies:

Reverse suggests: modeltime

Linking:

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