MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series Forecasting

Binqing Wu; Dongliang Cui; Ling Chen; Zongjiang Shang

MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series Forecasting

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arxiv 2401.09261 v2 pith:FWDR2IEA submitted 2024-01-17 cs.LG

Zongjiang Shang , Ling Chen , Binqing Wu , Dongliang Cui This is my paper · ORCID

classification cs.LG

keywords hypergraphforecastinginteractionslong-rangemodelingmshypermulti-scalepattern

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Demystifying interactions between temporal patterns of different scales is fundamental to precise long-range time series forecasting. However, previous works lack the ability to model high-order interactions. To promote more comprehensive pattern interaction modeling for long-range time series forecasting, we propose a Multi-Scale Hypergraph Transformer (MSHyper) framework. Specifically, a multi-scale hypergraph is introduced to provide foundations for modeling high-order pattern interactions. Then by treating hyperedges as nodes, we also build a hyperedge graph to enhance hypergraph modeling. In addition, a tri-stage message passing mechanism is introduced to aggregate pattern information and learn the interaction strength between temporal patterns of different scales. Extensive experiments on five real-world datasets demonstrate that MSHyper achieves state-of-the-art (SOTA) performance across various settings.

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Cited by 2 Pith papers

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MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting
cs.LG 2025-09 conditional novelty 6.0 of 10

MillGNN learns delayed (lead-lag) influences between time series and between groups of series at multiple grouping scales, reporting state-of-the-art forecast errors on 11 benchmarks.
ST-Hyper: Learning High-Order Dependencies Across Multiple Spatial-Temporal Scales for Multivariate Time Series Forecasting
cs.LG 2025-09 conditional novelty 5.0 of 10

ST-Hyper combines spatial-temporal pyramid feature extraction with adaptive sparse hypergraph learning and tri-phase propagation to achieve state-of-the-art results on six multivariate time series forecasting benchmarks.

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