SHENG PAN — accepted AI-conference papers

2026

It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks

Zhongzheng Qiao, SHENG PAN, Anni Wang, Viktoriya Zhukova, Yong Liu, Xudong Jiang +4

ICML 2026poster

Time series foundation models (TSFMs) are revolutionizing the forecasting landscape from specific dataset modeling to generalizable task evaluation. However, we contend that existing benchmarks exhibit common limitations in four dimensions: constrained data composition dominated by reused legacy sou…

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2026

TimeOmni-VL: Unified Models for Time Series Understanding and Generation

Tong Guan, SHENG PAN, Johan Barthelemy, Zhao Li, Yujun Cai, Cesare Alippi +2

ICML 2026poster

Recent time series modeling faces a sharp divide between numerical generation and semantic understanding, with research showing that generation models often rely on superficial pattern matching, while understanding-oriented models struggle with high-fidelity numerical output. Although unified multim…

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