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.


TSrepr 1.0.2 2018/11/21

  • New accuracy measure MAAPE (mean arctangent absolute percentage error) was added
  • Added new references to vignettes
  • Added new references to documentation
  • Fixed some bad alignments in documentation

TSrepr 1.0.1 2018/05/31

  • Created unit tests (by testthat) for all functions
  • Fixed vignette titles
  • New citation of package (CITATION file in \inst)

TSrepr 1.0.0 2018/01/26

  • First CRAN release

Reference manual

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1.1.0 by Peter Laurinec, a year ago

https://petolau.github.io/package/, https://github.com/PetoLau/TSrepr/

Report a bug at https://github.com/PetoLau/TSrepr/issues

Browse source code at https://github.com/cran/TSrepr

Authors: Peter Laurinec [aut, cre]

Documentation:   PDF Manual  

Task views: Time Series Analysis

GPL-3 | file LICENSE license

Imports Rcpp, MASS, quantreg, wavelets, mgcv, dtt

Suggests knitr, rmarkdown, ggplot2, data.table, moments, testthat

Linking to Rcpp

Suggested by modeltime.

See at CRAN