Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting

Although transformer-based methods have achieved great success in multi-scale temporal pattern interaction modeling, two key challenges limit their further development: (1) Individual time points contain less semantic information, and leveraging attention to model pair-wise interactions may cause the information utilization bottleneck. (2) Multiple inherent temporal variations (e. g. , rising, falling, and fluctuating) entangled in temporal patterns. To this end, we propose Adaptive Multi-Scale Hypergraph Transformer (Ada-MSHyper) for time series forecasting. Specifically, an adaptive hypergraph learning module is designed to provide foundations for modeling group-wise interactions, then a multi-scale interaction module is introduced to promote more comprehensive pattern interactions at different scales. In addition, a node and hyperedge constraint mechanism is introduced to cluster nodes with similar semantic information and differentiate the temporal variations within each scales. Extensive experiments on 11 real-world datasets demonstrate that Ada-MSHyper achieves state-of-the-art performance, reducing prediction errors by an average of 4. 56%, 10. 38%, and 4. 97% in MSE for long-range, short-range, and ultra-long-range time series forecasting, respectively. Code is available at https: //github. com/shangzongjiang/Ada-MSHyper.

Authors

• Zongjiang Shang Zhejiang University
• Ling Chen University of Technology Sydney
• Binqing Wu Zhejiang University
• Dongliang Cui

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