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20242026
most citedSSD-TS: Exploring the Potential of Linear State Space Models for Diffusion Models in Time Series Imputation

11 citations · 19 across the 29 of their papers we have counts for

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cs.LG2026

MUSE: Dependency-Aware Adaptation of a Frozen Vision Backbone for Multivariate Time Series Forecasting

Xinying Cai, Junkai Lu, Yuhan Zhu +3

Multivariate time-series forecasting is essential to many real-world applications. Recent large vision models (LVMs) offer a promising paradigm by transferring cross-domain visual…

cs.LG2026

Enhancing Irregular Time Series Forecasting with Continuous-Time Modeling Framework

Tianen Shen, Zhengyu Li, Yutong Li +4

Irregular multivariate time series are widely encountered in applications such as healthcare monitoring, human activity recognition, and environmental sensing. Their core challenge…

cs.LG2026

Adaptive Oscillatory-State Alignment for Time Series Forecasting

Zhangyao Song, Chaofeng Qu, Chao Zha +3

Long-term time series forecasting benefits from inductive biases that expose recurring temporal structure. Existing periodic forecasting methods typically model recurrence through…

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

GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variables

Zhengyu Li, Xiangfei Qiu, Yuhan Zhu +4

Exogenous variables offer valuable supplementary information for predicting future endogenous variables. Forecasting with exogenous variables needs to consider both past-to-future…

cs.LG2026

MEMTS: Internalizing Domain Knowledge via Parameterized Memory for Retrieval-Free Domain Adaptation of Time Series Foundation Models

Xiaoyun Yu, Li fan, Xiangfei Qiu +7

While Time Series Foundation Models (TSFMs) have demonstrated exceptional performance in generalized forecasting, their performance often degrades significantly when deployed in re…