Probabilistic Hypergraph Recurrent Neural Networks for Time-series Forecasting

Abstract

Leveraging graph structures for time-series forecasting has garnered significant attention due to their effective relationship modeling between nodes and their associated time-series. However, in scenarios entities communicate in a broadcasting manner, graph models fall short of pairwise modeling. Hypergraph models address this by capturing beyond-pairwise interactions among node time-series. Nevertheless, most hypergraph models overlook the dynamics between nodes and their incident hyperedges, assuming constant node-hyperedge connections. In this paper, we introduce a novel model, Probabilistic Hypergraph Recurrent Neural Networks (PHRNN), which leverages node-hyperedge dynamics for accurate time-series forecasting. PHRNN associates each timeseries with a node and models node interactions on a hypergraph, capturing beyond-pairwise interactions. Moreover, PHRNN learns a probabilistic hypergraph in which node-hyperedge relations are modeled as probabilistic distributions instead of fixed values, capturing dynamic node-hyperedge relations. PHRNN further integrates a prior knowledge KNN hypergraph as regularization when learning the probabilistic hypergraph structure. To the best of our knowledge, PHRNN is the first time-series forecasting model that incorporates hypergraph modeling and probabilistic relationship modeling. Forecasting results from extensive experiments show that PHRNN outperforms state-of-the-art graph and hypergraph baselines on real-world datasets.