Representing Multi-view Time-series Graph Structures for Multivariate Long-term Time-series Forecasting

Wzh Rslh, Jin Fan, Huifeng Wu, Danfeng Sun

Keywords: time series forecasting, deep learning, representational learning

TL;DR: An efficient, highly accurate, lightweight model for multivariate long-term time series forecasting.

Abstract: Multivariate long-term time-series forecasting task is a very challenging task in real-world application areas, such as electricity consumption and influenza-like illness forecasting. At present, researchers are focusing on designing robust and effective models, and have achieved good results. However, there are several issues with existing models that need to be overcome to ensure they provide optimal performance. First, the lack of a relationship structure between multivariate variables needs to be addressed. Second, most models only have a weak ability to capture local dynamic changes across the entire long-term time-series. And, third, the current models suffer from high computational complexity and unsatisfactory accuracy. To address these issues, we propose a novel method called Multi-view Time-series Graph Structure Representation (MTGSR) for multivariate long-term time-series forecasting tasks. MTGSR uses graph convolutional networks (GCNs) to construct topological relationships in the multivariate long-term time-series from three different perspectives: time, dimension, and crossing segments. Variation trends in the different dimensions of the multivariate long-term time-series are extracted through a difference operation so as to construct a topological map that reflects the correlations between the different dimensions. Then, to capture the dynamically changing characteristics of the fluctuation correlations between adjacent local sequences, MTGSR constructs a cross graph by calculating the correlation coefficients between adjacent local sequences. Extensive experiments on five different datasets show that MTGSR reduces errors by 20.41% over the state-of-the-art while maintaining linear complexity. Additionally, memory use is decreased by 66.52% and running time is reduced by 78.09%.

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