Hyperbolic Time Series Forecasting with Learnable Decomposition and Manifold Dynamics
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arXiv:2609.08286v1 Announce Type: new Abstract: Multi-scale modeling has become an effective approach for long-term time series forecasting, capturing temporal patterns that range from fine-grained l...
HypLTSF introduces a hyperbolic geometric framework for long‑term time series forecasting by embedding multi‑scale representations into the Poincaré ball. The approach imposes radial and angular constraints to align the geometry with temporal hierarchies, ensuring that coarser scales abstract finer ones and that related fine‑scale patterns share common ancestors. Experiments on forecasting benchmarks demonstrate that this explicit geometric modeling of the hierarchy yields state‑of‑the‑art performance.
HALO introduces a hyperspherical VAE to constrain continuous latent representations to a fixed‑radius shell, stabilizing numerical fluctuations. It then employs a masked autoregressive model that balances parallel decoding with temporal correlation learning, reducing inference steps and improving stability. Experiments show HALO achieves state‑of‑the‑art generation performance with significantly better inference efficiency compared to existing baselines.
arXiv:2608. 08675v1 Announce Type: new Abstract: Long-term time series forecasting benefits from preserving global structure such as trends and seasonality.
arXiv:2606. 08563v1 Announce Type: new Abstract: While global data-driven models excel at predicting continuous atmospheric variables, three-dimensional hydrometeor forecasting remains challenging due to the zero-inflated, long-tailed distributions of these variables.
arXiv:2606. 28553v1 Announce Type: new Abstract: In many real-world applications, such as retail sales, energy usage, and supply chain planning, forecasting is performed across hierarchical structures.