12 papers
Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis
Yisong Fu, Zezhi Shao, Chengqing Yu +4
We present Zeus, a unified tuning-free Time Series Foundation Model (TSFM) that delivers superior performance across diverse analysis tasks without any task-specific fine-tuning. U…
DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting
Siru Zhong, Yiqiu Liu, Zhiqing Cui +4
Deep time series models are vulnerable to noisy data ubiquitous in real-world applications. Existing robustness strategies either prune data or rely on costly prior quantification,…
PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting
Yangyou Liu, Zezhi Shao, Xinyu Chen +3
Time series forecasting under non-stationarity faces a fundamental tension between capturing stable representations and adapting to distribution shifts. Existing methods implicitly…
From Consistency to Complementarity: Aligned and Disentangled Multi-modal Learning for Time Series Understanding and Reasoning
Hang Ni, Weijia Zhang, Fei Wang +2
Advances in multi-modal large language models (MLLMs) have inspired time series understanding and reasoning tasks, that enable natural language querying over time series, producing…
HUTFormer: Hierarchical U-Net Transformer for Long-Term Traffic Forecasting
Zezhi Shao, Fei Wang, Tao Sun +7
Traffic forecasting, which aims to predict traffic conditions based on historical observations, has been an enduring research topic and is widely recognized as an essential compone…
APT: Affine Prototype-Timestamp For Time Series Forecasting Under Distribution Shift
Yujie Li, Zezhi Shao, Chengqing Yu +4
Time series forecasting under distribution shift remains challenging, as existing deep learning models often rely on local statistical normalization (e.g., mean and variance) that…