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Progress in Artificial Intelligence

Erschienen in:

05.06.2019 | Regular Paper

predtoolsTS: R package for streamlining time series forecasting

verfasst von: Francisco Charte, Alberto Vico, María D. Pérez-Godoy, Antonio J. Rivera

Erschienen in: Progress in Artificial Intelligence | Ausgabe 4/2019

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Abstract

Time series forecasting is a field of interest in many areas. Classically, statistical methods have been used to address this problem. In recent years, machine learning (ML) algorithms have been also applied with satisfactory results. However, ML software packages are not skilled to deal with raw sequences of temporal data, and therefore, it is necessary to transform these time series. This paper presents predtoolsTS, an R package that provides a uniform interface for applying both statistical and ML methods to time series forecasting. predtoolsTS comprises four modules: preprocessing, modeling, prediction and postprocessing, in order to deal with the whole process of time series forecasting.

Vorheriger Artikel Contrasting logical sequences in multi-relational learning

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The predtoolsTS package also relies on basic R functions and regression models in the caret package to accomplish its work.

Standard R methods, such as summary() and plot(), can be used with this object class.

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Titel: predtoolsTS: R package for streamlining time series forecasting
verfasst von: Francisco Charte
Alberto Vico
María D. Pérez-Godoy
Antonio J. Rivera
Publikationsdatum: 05.06.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Progress in Artificial Intelligence / Ausgabe 4/2019
Print ISSN: 2192-6352
Elektronische ISSN: 2192-6360
DOI: https://doi.org/10.1007/s13748-019-00193-z

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