Dynamic Hypergraph Representation Learning for Multivariate Time Series without Prior Knowledge

TL;DR

This paper introduces a novel method for constructing dynamic hypergraph representations from multivariate time series data without prior structural knowledge, enhancing the modeling of complex high-order relationships.

Contribution

It proposes a community detection-based approach combined with attention mechanisms to derive hypergraphs, enabling improved multivariate time series prediction.

Findings

Hypergraph representations improve prediction accuracy.

The method effectively uncovers high-order relationships.

It outperforms existing approaches on multiple datasets.

Abstract

Hypergraphs have the capacity to capture higher-dimensional relationships among entities across various domains, making them a subject of growing interest within the research community for understanding the structure and dynamics of complex systems. However, a key challenge is the derivation of hypergraph representations from time series data in situations where the structure of the hypergraph is limited or absent. In this study, we propose a model that constructs a dynamic hypergraph representation for multivariate time series without relying on prior knowledge of the data. This is achieved by applying community detection to the time series and transforming the resulting communities, obtained through an attention mechanism, into a hypergraph using a clique-based technique. Hypergraph representations are derived from different time series datasets, and the resulting hypergraphs are then used by a Dynamic Hypergraph Attention Convolution Network (DHACN) for multivariate time series predictions. This research advances the field of hypergraph representation by introducing a novel approach that is better suited to uncover high-order relationships without prior knowledge.