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dc.contributor.authorLazcano de Rojas, Ana
dc.date.accessioned2024-02-20T15:41:12Z
dc.date.available2024-02-20T15:41:12Z
dc.date.issued2023
dc.identifier.issn2473-6988spa
dc.identifier.urihttps://hdl.handle.net/10641/4055
dc.description.abstractThe performance of neural networks and statistical models in time series prediction is conditioned by the amount of data available. The lack of observations is one of the main factors influencing the representativeness of the underlying patterns and trends. Using data augmentation techniques based on classical statistical techniques and neural networks, it is possible to generate additional observations and improve the accuracy of the predictions. The particular characteristics of economic time series make it necessary that data augmentation techniques do not significantly influence these characteristics, this fact would alter the quality of the details in the study. This paper analyzes the performance obtained by two data augmentation techniques applied to a time series and finally processed by an ARIMA model and a neural network model to make predictions. The results show a significant improvement in the predictions by the time series augmented by traditional interpolation techniques, obtaining a better fit and correlation with the original series.spa
dc.language.isoengspa
dc.publisherAIMS Mathematicsspa
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectTime series forecastingspa
dc.subjectFinancial forecastingspa
dc.subjectData augmentationspa
dc.titleData augmentation in economic time series: Behavior and improvements in predictions.spa
dc.typejournal articlespa
dc.type.hasVersionAMspa
dc.rights.accessRightsopen accessspa
dc.description.extent1015 KBspa
dc.identifier.doi10.3934/math.20231251spa
dc.relation.publisherversionhttps://www.aimspress.com/article/doi/10.3934/math.20231251spa


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Atribución-NoComercial-SinDerivadas 3.0 España
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España