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

• Type: Article in International Conference Proceedings Book
• Date: 2013

Authors

Patrícia Ramos (Author) Other; Nuno Santos (Author) Other; Rui Rebelo (Author) Other; Bernardo Almada Lobo (Author) FEUP

Conference proceedings International

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

Pages: 1-8

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

Porto, Portugal, 26 a 28 de junho de 2013

Scientific classification

FOS: Engineering and technology

Other information

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.

Language: English

Type (Professor's evaluation): Scientific