Performance of state space and ARIMA models for consumer retail sales forecasting

dc.contributor.author Patrícia Ramos en
dc.contributor.author Nicolau Filipe Santos en
dc.contributor.author Rui Diogo Rebelo en
dc.date.accessioned 2018-01-17T14:29:01Z
dc.date.available 2018-01-17T14:29:01Z
dc.date.issued 2015 en
dc.description.abstract Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail businesses. For profitable retail businesses, accurate demand forecasting is crucial in organizing and planning production, purchasing, transportation and labor force. Retail sales series belong to a special type of time series that typically contain trend and seasonal patterns, presenting challenges in developing effective forecasting models. This work compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. On both methodologies the model with the minimum value of Akaike's Information Criteria for the in-sample period was selected from all admissible models for further evaluation in the out-of-sample. Both one-step and multiple-step forecasts were produced. The results show that when an automatic algorithm the overall out-of-sample forecasting performance of state space and ARIMA models evaluated via RMSE, MAE and MAPE is quite similar on both one-step and multi-step forecasts. We also conclude that state space and ARIMA produce coverage probabilities that are close to the nominal rates for both one-step and multi-step forecasts. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/6712
dc.identifier.uri http://dx.doi.org/10.1016/j.rcim.2014.12.015 en
dc.language eng en
dc.relation 3160 en
dc.relation 5154 en
dc.relation 5234 en
dc.rights info:eu-repo/semantics/embargoedAccess en
dc.title Performance of state space and ARIMA models for consumer retail sales forecasting en
dc.type article en
dc.type Publication en
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