Evaluation Procedures for Forecasting with Spatiotemporal Data

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

Oliveira, M (Autor) Outra; Torgo, L (Autor) FCUP; Costa, VS (Autor) FCUP

Revista

Título: Mathematics

Vol. 9 Nº 12

Página Final: 691

Editora: MDPI

Indexação

ISI Web of Knowledge - 17 Citações

Scopus - 18 Citações

Outras Informações

ID Authenticus: P-00T-P3N

DOI: 10.3390/math9060691

Abstract (EN): The increasing use of sensor networks has led to an ever larger number of available spatiotemporal datasets. Forecasting applications using this type of data are frequently motivated by important domains such as environmental monitoring. Being able to properly assess the performance of different forecasting approaches is fundamental to achieve progress. However, traditional performance estimation procedures, such as cross-validation, face challenges due to the implicit dependence between observations in spatiotemporal datasets. In this paper, we empirically compare several variants of cross-validation (CV) and out-of-sample (OOS) performance estimation procedures, using both artificially generated and real-world spatiotemporal datasets. Our results show both CV and OOS reporting useful estimates, but they suggest that blocking data in space and/or in time may be useful in mitigating CV's bias to underestimate error. Overall, our study shows the importance of considering data dependencies when estimating the performance of spatiotemporal forecasting models.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 27