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.
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Notes
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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This work was partially supported by the project TIN2015-68854-R (FEDER Founds) of the Spanish Ministry of Economy and Competitiveness.
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Charte, F., Vico, A., Pérez-Godoy, M.D. et al. predtoolsTS: R package for streamlining time series forecasting. Prog Artif Intell 8, 505–510 (2019). https://doi.org/10.1007/s13748-019-00193-z
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DOI: https://doi.org/10.1007/s13748-019-00193-z