@article {777,
	title = {predtoolsTS: R package for streamlining time series forecasting},
	journal = {Progress in Artificial Intelligence},
	volume = {8},
	year = {2019},
	note = {TIN2015-68854-R},
	month = {06/2019},
	pages = {505{\textendash}510},
	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.},
	keywords = {machine learning, R, time series forecasting},
	doi = {https://doi.org/10.1007/s13748-019-00193-z},
	author = {Francisco Charte and Alberto Vico and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas}
}
