Evaluation Procedures for Forecasting with Spatio-Temporal Data

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2019

Autores

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

Ata de Conferência Internacional

Título: MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2018, PT I

Páginas: 703-718

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

Dublin, IRELAND, SEP 10-14, 2018

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 6 Citações

Outras Informações

ID Authenticus: P-00Q-7W2

DOI: 10.1007/978-3-030-10925-7_43

Abstract (EN): The amount of available spatio-temporal data has been increasing as large-scale data collection (e.g., from geosensor networks) becomes more prevalent. This has led to an increase in spatio-temporal forecasting applications using geo-referenced time series data motivated by important domains such as environmental monitoring (e.g., air pollution index, forest fire risk prediction). Being able to properly assess the performance of new forecasting approaches is fundamental to achieve progress. However, the dependence between observations that the spatio-temporal context implies, besides being challenging in the modelling step, also raises issues for performance estimation as indicated by previous work. In this paper, we empirically compare several variants of cross-validation (CV) and out-of-sample (OOS) performance estimation procedures that respect data ordering, using both artificially generated and real-world spatio-temporal data sets. Our results show both CV and OOS reporting useful estimates. Further, they suggest that blocking may be useful in addressing CV's bias to underestimate error. OOS can be very sensitive to test size, as expected, but estimates can be improved by careful management of the temporal dimension in training.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 16