Lag Selection for Univariate Time Series Forecasting Using Deep Learning: An Empirical Study

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024

Autores

Leites, J (Autor) Outra; Cerqueira, V (Autor) FEUP; Carlos Soares (Autor) FEUP

Ata de Conferência Internacional

Título: Progress in Artificial Intelligence - 23rd EPIA Conference on Artificial Intelligence, EPIA 2024, Viana do Castelo, Portugal, September 3-6, 2024, Proceedings, Part III

Páginas: 321-332

23rd EPIA Conference on Artificial Intelligence, EPIA 2024

Viana do Castelo, 3 September 2024 through 6 September 2024

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-010-NB9

DOI: 10.1007/978-3-031-73503-5_26

Abstract (EN): Most forecasting methods use recent past observations (lags) to model the future values of univariate time series. Selecting an adequate number of lags is important for training accurate forecasting models. Several approaches and heuristics have been devised to solve this task. However, there is no consensus about what the best approach is. Besides, lag selection procedures have been developed based on local models and classical forecasting techniques such as ARIMA. We bridge this gap in the literature by carrying out an extensive empirical analysis of different lag selection methods. We focus on deep learning methods trained in a global approach, i.e., on datasets comprising multiple univariate time series. Specifically, we use NHITS, a recently proposed architecture that has shown competitive forecasting performance. The experiments were carried out using three benchmark databases that contain a total of 2411 univariate time series. The results indicate that the lag size is a relevant parameter for accurate forecasts. In particular, excessively small or excessively large lag sizes have a considerable negative impact on forecasting performance. Cross-validation approaches show the best performance for lag selection, but this performance is comparable with simple heuristics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11