Author

Bin Yang

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#machine learning Preprint Sep 2026

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

Most existing time series generators rely on a two-stage modeling paradigm: the first stage learns discrete latent representations of time series; the second stage performs autoregressive modeling on these discrete latents through next token prediction. However, this paradigm suffers from two stage-specific limitations...

Chun-Yi Hou, Xiangfei Qiu, Han-Yin Cheng et al. · 0 citations

#machine learning Dec 2025

FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting

In this work, we introduce FLAME, a family of extremely lightweight and capable Time Series Foundation Models, which support versatile forecasting tasks via generative probabilistic modeling, while ensuring both efficiency and robustness. FLAME utilizes the Legendre Memory for strong generalization capabilities. By ada...

Xingjian Wu, Han-Yin Cheng, Xiangfei Qiu et al. · arXiv.org · 4 citations

#machine learning Preprint Sep 2026

Aurora-X: Built for Extreme Time Series Forecasting

A novel pattern-guided mixture-of-experts that expands model capacity through sparse activation and uses shallow patch similarities to constrain deep-layer routing, guiding expert specialization across heterogeneous time series and an implicit quantile network head that predicts arbitrary quantiles to characterize pred...

Xingjian Wu, Chen-Juan Guo, Xiangfei Qiu et al. · 0 citations