Hyperbolic Time Series Forecasting with Learnable Decomposition and Manifold Dynamics
Multiscale structure is central to time series forecasting: temporal signals contain slowly varying trends, intermediate seasonal patterns, and high-frequency residuals. Traditional forecasters model this multiscale structure in Euclidean space, without representing the ordering it induces among components. We identify that cascaded decomposition orders components by their characteristic frequencies, forming a natural frequency hierarchy. This hierarchy aligns with the exponential volume growth of hyperbolic space, which provides increasing representational capacity with radius and naturally accommodates nested hierarchies. We propose HyperForecast, the first long-term time series forecas...
| Authors | Sarang Rajendra Patil, Vishal Fenn, Mengjia Xu |
|---|---|
| Primary category | cs.LG |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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