Evaluating the Forecasting Accuracy of Pure Time Series Models on Retail Data

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2016

Autores

Patrícia Ramos (Autor) FEUP; José Manuel Oliveira (Autor) FEP; Rebelo, R (Autor) Outra

Ata de Conferência Internacional

Título: ADVANCES IN MANUFACTURING TECHNOLOGY XXX

Páginas: 489-494

14th International Conference on Manufacturing Research (ICMR)

Loughborough Univ, Loughborough, ENGLAND, SEP 06-08, 2016

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00M-3MM

DOI: 10.3233/978-1-61499-668-2-489

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 supply chains. For profitable retail businesses, accurate sales forecasting is crucial in organizing and planning purchasing, production, transportation and labor force. Retail sales series belong to a special type of time series that typically contain strong trend and seasonal patterns, presenting challenges in developing effective forecasting models. This paper 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. An approach based on cross-validation is used to identify automatically appropriate state space and ARIMA models. The forecasting performance of these models is also compared by examining the out-of-sample forecasts. The results indicate that the overall out-of-sample forecasting performance of ARIMA models evaluated via RMSE, MAE and MAPE is better than state space models. The performance of both forecasting methodologies in producing forecast intervals was also evaluated and the results indicate that ARIMA produces slightly better coverage probabilities than state space models for the nominal 95% forecast intervals. For the nominal 80% forecast intervals the performance of state space models is slightly better.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6