REPRESENTING MULTI-VIEW TIME-SERIES GRAPH STRUCUTRES 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%.

1. INTRODUCTION

In reality, a large amount of time-series data is produced in various fields, such as weather forecasting (Hewage et al., 2021; Rasp et al., 2020) , electricity power planning (Qader et al., 2022; Oreshkin et al., 2021) , disease propagation prejudgment (Li et al., 2021; Zimmer & Yaesoubi, 2020) , and more. Although challenging to model the long-term relationships and multivariate correlations within these real-world time-series are important elements of most practical forecasting tasks involving these data. Thus, in this paper, we focus on multivariate long-term time-series forecasting task, which has higher requirements for models than ordinary time-series forecasting tasks. In recent years, deep learning models have been thoroughly investigated for their power at multivariate longseries forecasting tasks with many achieving good results (Liu et al., 2021; Torres et al., 2021; Lim & Zohren, 2021) . For example, Transformer-based models, the mainstream framework for multivariate long-term time-series forecasting tasks, relies on multi-head self-attention as a core mechanism for extracting powerful characteristics from historical data (Nikita et al., 2020; Zhou et al., 2021; Xu et al., 2021; Zhou et al., 2022) . These characteristics are then analyzed to predict long sequences containing data from farther in the future. However, there are still several extremely challenging issues in multivariate long-term time-series forecasting tasks that need to be addressed. First, existing models do not construct relationships between multivariate variables. Rather, they pay more attention to capturing the temporal features of the series, which means they simply use dimensional mappings to extract blurry relationships

