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20232026
most citediTransformer: Inverted Transformers Are Effective for Time Series Forecasting

383 citations · 469 across the 14 of their papers we have counts for

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cs.AI2026

Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling

Yong Liu, Xingjian Su, Shiyu Wang +7

We introduce Timer-S1, a strong Mixture-of-Experts (MoE) time series foundation model with 8.3B total parameters, 0.75B activated parameters for each token, and a context length of…

cs.AI2026

HiMAP-Travel: Hierarchical Multi-Agent Planning for Long-Horizon Constrained Travel

The Viet Bui, Wenjun Li, Yong Liu

Sequential LLM agents fail on long-horizon planning with hard constraints like budgets and diversity requirements. As planning progresses and context grows, these agents drift from…

cs.LG2026

DiTS: Multimodal Diffusion Transformers Are Time Series Forecasters

Haoran Zhang, Haixuan Liu, Yong Liu +4

While generative modeling facilitates probabilistic time series forecasting, incorporating heterogeneous exogenous information remains challenging. Diffusion Transformers (DiT) pro…

cs.LG2026

Parallel Complex Diffusion for Scalable Time Series Generation

Rongyao Cai, Yuxi Wan, Kexin Zhang +4

Diffusion models learn data distributions indirectly through denoising, making the difficulty of generative modeling closely tied to the dependency structure of data. For time seri…

cs.LG2026

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

Zhongzheng Qiao, Sheng Pan, Anni Wang +7

Time series foundation models (TSFMs) are revolutionizing the forecasting landscape from specific dataset modeling to generalizable task evaluation. However, we contend that existi…