arXiv AI By Sarang Rajendra Patil, Vishal Fenn, Mengjia Xu

Hyperbolic Time Series Forecasting with Learnable Decomposition and Manifold Dynamics

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

Hugging Face Trending Papers
Sep 8

HypLTSF: A Hyperbolic Geometric View of Multi-Scale Hierarchies for Long-Term Time Series Forecasting

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.

arXiv Machine Learning
Sep 30

HALO: Enhancing Time Series Generation via Hyperspherical Latents and Masked AutoregRessive Modeling

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.

By Chunyi Hou, Xiangfei Qiu, Hanyin Cheng, Yutong Li, Bin Yang