TSPred: A framework for nonstationary time series prediction - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Journal Articles Neurocomputing Year : 2022

TSPred: A framework for nonstationary time series prediction

Abstract

The nonstationary time series prediction is challenging since it demands knowledge of both data transformation and prediction methods. This paper presents TSPred, a framework for nonstationary time series prediction. It differs from the mainstream frameworks since it establishes a prediction process that seamlessly integrates nonstationary time series transformations with state-of-the-art statistical and machine learning methods. It is made available as an R-package, which provides functions for defining and conducting time series prediction, including data pre(post)processing, decomposition, modeling, prediction, and accuracy assessment. Besides, TSPred enables user-defined methods, which significantly expands the applicability of the framework.
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Dates and versions

lirmm-03452170 , version 1 (26-11-2021)

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Rebecca Salles, Esther Pacitti, Eduardo Bezerra, Fabio Porto, Eduardo Ogasawara. TSPred: A framework for nonstationary time series prediction. Neurocomputing, 2022, 467, pp.197-202. ⟨10.1016/j.neucom.2021.09.067⟩. ⟨lirmm-03452170⟩
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