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From the 1 of 31 linked papers with an AI index.

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31 papers

cs.LG2026

Enhancing Irregular Time Series Forecasting with Continuous-Time Modeling Framework

Tianen Shen, Zhengyu Li, Yutong Li +4

The paper introduces WrapFlow, a framework that directly tokenizes irregular multivariate time‑series observations into continuous‑time tokens and uses a Transformer with a simulat…

cs.LG2026

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing

Ronghui Xu, Tongxin Wu, Guozhen Zhang +4

Day-ahead wind power forecasting is essential for cost-effective power-system operation. It is primarily driven by future meteorological conditions while retaining temporal depende…

cs.LG2026

CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching

Xingjian Wu, Xiangfei Qiu, Zhengyu Li +5

Anomaly detection in multivariate time series is challenging as heterogeneous subsequence anomalies may occur. Reconstruction-based methods, which focus on learning normal patterns…

stat.ML2026

HyFAD: Hybrid Time-Frequency Diffusion with Frequency-Aware Embedding for Time Series Imputation

Hongfan Gao, Wangmeng Shen, Bin Yang +1

Diffusion models have demonstrated strong performance in time series modeling due to their ability to progressively capture complex data distributions through iterative denoising.…

cs.LG2026

Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models

Xingjian Wu, Junkai Lu, Siyu Yan +4

Recent advances in Large Language Models (LLMs) have catalyzed the development of multi-agent systems (MAS) for complex reasoning tasks. However, existing MAS typically rely on pre…

cs.LG2026

DAG: A Dual Correlation Network for Time Series Forecasting with Exogenous Variables

Xiangfei Qiu, Yuhan Zhu, Zhengyu Li +3

Time series forecasting is essential in various domains. Compared to relying solely on endogenous variables (i.e., target variables), considering exogenous variables (i.e., covaria…