A Multi-Hierarchical attention-based prediction method on Time Series with spatio-temporal context among variables

Zhuo-Lin Li, Jie Yu, Xiao-Lin Zhang, Ling-Yu Xu and Bao-Gang Jin

Physica A: Statistical Mechanics and its Applications, 2022, vol. 602, issue C

Abstract: Multi-scale prediction of multivariate time series in Earth system science is a challenging problem due to the task with spatio-temporal context between multi-type variables. For example Offshore wind is influenced by the spatio-temporal features of own and other ocean elements. Existing methods do not fully exploit the spatio-temporal features and influence information of non-predictive series on target series, so the major challenge is how to effectively extract these features and integrate them into an end-to-end network. In this paper, we propose a multi-hierarchical attention network (MHA), which exploits triple attention mechanisms to capture the correlations of multi-type variables in identical spacetime, different space at the same time and different spacetime. The first hierarchical of attention captures the coupling mechanisms of multi-type variables at the same spacetime. The second hierarchical of attention extracts spatial relationships between target and non-predictive series at each moment. The third hierarchical of attention selects the relevant information across all time steps to learn the time dependence of data. Experiments on two different real-world datasets, viz., Offshore wind data and Ocean current data, demonstrate the effectiveness and robustness of the developed approach. Specifically, the triple attention can successfully capture internal patterns between variables in different spacetime. Overall, our proposed method not only improves the prediction performance of multivariate time series in Earth system science, but also reveals interaction patterns between variables from a data-driven perspective.

Keywords: Time series prediction; Multi-type variables; Spatio-temporal context; Deep learning; Attention mechanism (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437122004460
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:602:y:2022:i:c:s0378437122004460

DOI: 10.1016/j.physa.2022.127664

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().