The bias in reversing the Box-Cox transformation in time series forecasting: An empirical study based on neural networks

dc.contributorUniversidade Estadual Paulista (Unesp)
dc.creatorCosta, Alexandre Fructuoso da [UNESP]
dc.creatorCrepaldi, Antonio Fernando [UNESP]
dc.date2014-12-03T13:11:47Z
dc.date2014-12-03T13:11:47Z
dc.date2014-07-20
dc.date.accessioned2023-09-09T10:17:21Z
dc.date.available2023-09-09T10:17:21Z
dc.identifierhttp://dx.doi.org/10.1016/j.neucom.2014.01.004
dc.identifierNeurocomputing. Amsterdam: Elsevier Science Bv, v. 136, p. 281-288, 2014.
dc.identifier0925-2312
dc.identifierhttp://hdl.handle.net/11449/113544
dc.identifier10.1016/j.neucom.2014.01.004
dc.identifierWOS:000335708800028
dc.identifier9211187637499715
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8763140
dc.descriptionThe Box-Cox transformation is a technique mostly utilized to turn the probabilistic distribution of a time series data into approximately normal. And this helps statistical and neural models to perform more accurate forecastings. However, it introduces a bias when the reversion of the transformation is conducted with the predicted data. The statistical methods to perform a bias-free reversion require, necessarily, the assumption of Gaussianity of the transformed data distribution, which is a rare event in real-world time series. So, the aim of this study was to provide an effective method of removing the bias when the reversion of the Box-Cox transformation is executed. Thus, the developed method is based on a focused time lagged feedforward neural network, which does not require any assumption about the transformed data distribution. Therefore, to evaluate the performance of the proposed method, numerical simulations were conducted and the Mean Absolute Percentage Error, the Theil Inequality Index and the Signal-to-Noise ratio of 20-step-ahead forecasts of 40 time series were compared, and the results obtained indicate that the proposed reversion method is valid and justifies new studies. (C) 2014 Elsevier B.V. All rights reserved.
dc.descriptionUniv Estadual Paulista, UNESP, Fac Engn Baum, Dept Prod Engn, BR-17033360 Bauru, SP, Brazil
dc.descriptionUniv Estadual Paulista, UNESP, Fac Engn Baum, Dept Prod Engn, BR-17033360 Bauru, SP, Brazil
dc.format281-288
dc.languageeng
dc.publisherElsevier B.V.
dc.relationNeurocomputing
dc.relation3.241
dc.relation1,073
dc.rightsAcesso restrito
dc.sourceWeb of Science
dc.subjectBox-Cox transformation
dc.subjectNeural networks
dc.subjectTime series forecasting
dc.subjectFinancial markets
dc.titleThe bias in reversing the Box-Cox transformation in time series forecasting: An empirical study based on neural networks
dc.typeArtigo


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