Comparison of Forecasting Performance of ARIMA LSTM and HYBRID Models for The Sales Volume Budget of a Manufacturing Enterprise

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dc.contributor.authorTemur, Ayse Soy
dc.contributor.authorYildiz, Sule
dc.date.accessioned2021-12-01T18:48:35Z
dc.date.available2021-12-01T18:48:35Z
dc.date.issued2021
dc.department[Belirlenecek]en_US
dc.description.abstractThis study aims to create a monthly sales quantity budget by making use of the previous income data of an enterprise operating within the construction sector, which is considered the locomotive of the economy. For estimating time-series of sales as a linear model ARIMA (Auto-Regressive Integrated Moving Average), as nonlinear model LSTM (Long Short-Term Memory) and a HYBRID (LSTM and ARIMA) model built to improve system performance compared to a single model was used. As a result of the study, Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) values obtained from each of the methods used in the application were compared, and a monthly sales volume budget was created for 2017 with all the methods used. When the MAPE and MSE values obtained from each of these methods were compared, the best performance was the Hybrid model that gave the lowest error, and in addition, the fact that all of the application models got very realistic results by using the historical data showed the success of the predictions..en_US
dc.identifier.doi10.26650/ibr.2021.51.0117
dc.identifier.endpage45en_US
dc.identifier.issn2630-5488
dc.identifier.issue1en_US
dc.identifier.startpage15en_US
dc.identifier.urihttps://doi.org/10.26650/ibr.2021.51.0117
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10565
dc.identifier.volume50en_US
dc.identifier.wosWOS:000681023600002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherIstanbul Univ, Sch Businessen_US
dc.relation.ispartofIstanbul Business Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSales Budgeten_US
dc.subjectTime Series Forecasten_US
dc.subjectHybrid Modelen_US
dc.subjectARIMAen_US
dc.subjectLSTMen_US
dc.subjectArtificial Neural-Networksen_US
dc.subjectTime-Seriesen_US
dc.subjectAnnen_US
dc.subjectPricesen_US
dc.titleComparison of Forecasting Performance of ARIMA LSTM and HYBRID Models for The Sales Volume Budget of a Manufacturing Enterpriseen_US
dc.typeArticleen_US

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