An Experimental Study on Predictive Models Using Hierarchical Time Series

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2015

Autores

Silva, AM (Autor) Outra; Rita Ribeiro (Autor) FCUP; João Gama (Autor) FEP

Ata de Conferência Internacional

Título: PROGRESS IN ARTIFICIAL INTELLIGENCE

Páginas: 501-512

17th Portuguese Conference on Artificial Intelligence (EPIA)

Univ Coimbra, Coimbra, PORTUGAL, SEP 08-11, 2015

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00G-SYP

DOI: 10.1007/978-3-319-23485-4_50

Abstract (EN): Planning strategies play an important role in companies' management. In the decision-making process, one of the main important goals is sales forecasting. They are important for stocks planing, shop space maintenance, promotions, etc. Sales forecasting use historical data to make reliable projections for the future. In the retail sector, data has a hierarchical structure. Products are organized in hierarchical groups that reflect the business structure. In this work we present a case study, using real data, from a Portuguese leader retail company. We experimentally evaluate standard approaches for sales forecasting and compare against models that explore the hierarchical structure of the products. Moreover, we evaluate different methods to combine predictions for the different hierarchical levels. The results show that exploiting the hierarchical structure present in the data systematically reduces the error of the forecasts.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12