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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2015

Autores

Patrícia Ramos (Autor) FEUP; Santos, N (Autor) Outra; Rebelo, R (Autor) Outra

Revista

Título: Robotics and Computer-Integrated Manufacturing

Vol. 34

Páginas: 151-163

ISSN: 0736-5845

Editora: Elsevier

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00A-7X1

DOI: 10.1016/j.rcim.2014.12.015

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13