Biased resampling strategies for imbalanced spatio-temporal forecasting

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

Autores

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

Revista

Título: International Journal of Data Science and Analytics

Vol. 12 Nº 2

Páginas: 205-228

ISSN: 2364-415X

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 2 Citações

Outras Informações

ID Authenticus: P-00V-34Q

DOI: 10.1007/s41060-021-00256-2

Abstract (EN): Extreme and rare events, such as spikes in air pollution or abnormal weather conditions, can have serious repercussions. Many of these sorts of events develop through spatio-temporal processes. Timely and accurate predictions are a most valuable tool in addressing their impact. We propose a new set of resampling strategies for imbalanced spatio-temporal forecasting tasks, which introduce bias into formerly random processes. This bias is a combination of a spatial and a temporal weight, which can be either static or relevance-aware, and includes a hyper-parameter that regulates the relative importance of the temporal and spatial dimensions in the selection of observations during under- or over-sampling. We test and compare our proposals against standard versions of the strategies on 10 different geo-referenced numeric time series, using 3 distinct off-the-shelf learning algorithms. Experimental results show that our proposals provide an advantage over random resampling strategies in imbalanced numerical spatio-temporal forecasting tasks.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 24