Robust Preprocessing of Time Series Data

Methods for handling the missing values outliers are introduced in this package. The recognized missing values and outliers are replaced using a model-based approach. The model may consist of both autoregressive components and external regressors. The methods work robust and efficient, and they are fully tunable. The primary motivation for writing the package was preprocessing of the energy systems data, e.g. power plant production time series, but the package could be used with any time series data.


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install.packages("tsrobprep")

0.3.1 by Michał Narajewski, 2 months ago


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


Authors: Michał Narajewski [aut, cre] , Jens Kley-Holsteg [aut] , Florian Ziel [aut]


Documentation:   PDF Manual  


Task views: Time Series Analysis


MIT + file LICENSE license


Imports glmnet, MASS, Matrix, mclust, quantreg, splines, textTinyR, zoo


See at CRAN