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dc.rights.licenseAtribución 4.0 Internacional (CC BY 4.0)
dc.contributor.authorSoto-Ferrari, Milton
dc.contributor.authorChams-Anturi, Odette
dc.contributor.authorEscorcia-Caballero, Juan P.
dc.date.accessioned2020-12-26T16:22:36Z
dc.date.available2020-12-26T16:22:36Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/11323/7635
dc.description.abstractThis research focuses on the development of an automated forecasting procedure that implement State Space (SS), Auto Regressive Integrated Moving Average (ARIMA), and Neural Networks (NN) to identify the best forecasting strategy for time series with numerous patterns. The proposed approach is applied on multiple time series exhibiting different series patterns from the M4 competition. Based on our study, the performance of ARIMA models showed superior results when compared to the ETS performance for seasonal data. In addition, NN and ARIMA showed a higher performance for cyclical and non-stationary data. NN performance was competitive in all types of data patterns. ARIMA stepwise selection procedure proved to be the most accurate in general for all the series. This delimited development is part of a comprehensive application that will encompass a dashboard tool designed to automatize forecasting procedures of different types of time series presented in the industry Keywordsspa
dc.format.extent10 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherIEOM Society Internationalspa
dc.rights© IEOM Society International*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.sourcehttps://www.researchgate.net/publication/345914831_A_Time-Series_Forecasting_Performance_Comparison_for_Neural_Networks_with_State_Space_and_ARIMA_Modelsspa
dc.titleA time-series forecasting performance comparison for neural networks with state space and ARIMA modelsspa
dc.typeArtículo de revistaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.publisher.placeUnited Stateseng
dc.relation.ispartofjournalConference: International Conference on Industrial Engineering and Operations Managementeng
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dc.relation.referencesHyndman, R., Koehler, A., Ord, J. and Snyder, R. (2008), “Forecasting with exponential smoothing: The state space approach”, Springer Science & Business Media.eng
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dc.relation.referencesMOFC. (2018), “M4 Competition”, available at: https://mofc.unic.ac.cy/m4/ (accessed 25 July 2020).eng
dc.relation.referencesRamos, P., Santos, N. and Rebelo, R. (2015), “Performance of state space and ARIMA models for consumer retail sales forecasting”, Robotics and Computer-Integrated Manufacturing, Vol. 34, pp. 151–163.eng
dc.relation.referencesSeifert, M., Siemsen, E., Hadida, A. and Eisingerich, A. (2015), “Effective judgmental forecasting in the context of fashion products”, Journal of Operations Management, Vol. 36 No. 1, pp. 33–45.eng
dc.relation.referencesSeries, B.G.J.G.T. (1970), “Analysis: Forecasting and control”, San Francisco: Holden Day Press.eng
dc.relation.referencesSoto-Ferrari, M., Chams-Anturi, O., Escorcia-Caballero, J.P., Hussain, N. and Khan, M. (2019), “Evaluation of bottom-up and top-down strategies for aggregated forecasts: State space models and arima applications”, In International Conference on Computational Logistics. Springer, Cham, pp. 413–427.eng
dc.relation.referencesWidiarta, H., Viswanathan, S. and Piplani, R. (2008), “Forecasting item-level demands: An analytical evaluation of top–down versus bottom–up forecasting in a production-planning framework”, Journal of Management Mathematics, Vol. 19 No. 2, pp. 207–218.eng
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dc.subject.proposalForecastingeng
dc.subject.proposalState spaceeng
dc.subject.proposalARIMAeng
dc.subject.proposalNeural networkseng
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1eng
dc.type.contentTexteng
dc.type.driverinfo:eu-repo/semantics/articleeng
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTeng
dc.type.versioninfo:eu-repo/semantics/acceptedVersioneng
dc.relation.citationendpage164
dc.relation.citationstartpage155
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaeng
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2eng


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Atribución 4.0 Internacional (CC BY 4.0)
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