Capturing combination patterns of long- and short-term dependencies in multivariate time series forecasting

Wen Song, Shigeru Fujimura*

*この研究の対応する著者

研究成果: Article査読

抄録

Multivariate time series forecasting has typically been a relevant and interesting topic in many fields, including economics, electricity consumption, solar energy, and traffic management. In these domains, owing to the complex dependencies among multiple variables and the mixed dependencies in the time dimension, it is challenging to forecast a multivariate time series precisely. Furthermore, most of the forecasting methods fail to capture the mixed influence of the different time-length dependencies among multiple variables. In this paper, a new deep learning framework is proposed for dealing with this challenging problem, named as mixed dependence time-series network (MDTNet). In this framework, stacked dilated convolutions and recurrent units are applied to extract the complex patterns in the long- and short-term mixed dependencies among multiple variables. The experiments show that our proposed framework yields significant results, outperforming the state-of-the-art baseline methods on three of the four benchmark datasets in large horizons and achieving a competitive performance in short horizons on all the benchmark datasets.

本文言語English
ページ(範囲)72-82
ページ数11
ジャーナルNeurocomputing
464
DOI
出版ステータスPublished - 2021 11 13

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 認知神経科学
  • 人工知能

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