A Comparative Performance Study of State Space Models and ARIMA Models for Aggregate Retail Sales Forecasting

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2013

Autores

Patrícia Ramos (Autor) Outra; Nuno Santos (Autor) Outra; Rui Rebelo (Autor) Outra; Bernardo Almada Lobo (Autor) FEUP

Ata de Conferência Internacional

Título: 23th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2013

Páginas: 1-8

23th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2013

Porto, Portugal, 26 a 28 de junho de 2013

Classificação Científica

FOS: Ciências da engenharia e tecnologias

Outras Informações

Resumo (PT): 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 purchasing, production, 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 the innovations state space models and the 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. The results indicate that the ARIMA seasonal models outperform the state space models in out-of-sample forecasting.

Idioma: Inglês

Tipo (Avaliação Docente): Científica