3 papers
cs.LG2026
QiYao-M: Multimodal Time Series Foundation Model with Role-Aware Modeling of Endogenous and Exogenous Modalities
Hanyin Cheng, Linfeng Wang, Zhengbo Qu +7
Existing multimodal time series foundation models (TSFMs) typically model heterogeneous modalities through largely shared mechanisms, overlooking the distinct forecasting roles of…
cs.AI2026
Supervision Recovery for Time Series Anomaly Detection via Context-Anchored Pairing
Yifei Gao, Tian Lan, Yimeng Lu +5
Time series anomaly detection (TSAD) remains challenging not only because anomaly labels are scarce, but also because temporal anomalies are highly context-dependent. Existing meth…
cs.LG2026
Empowering Time Series Analysis with Large-Scale Multimodal Pretraining
Peng Chen, Siyuan Wang, Shiyan Hu +7
While existing time series foundation models primarily rely on large-scale unimodal pretraining, they lack complementary modalities to enhance time series understanding. Building m…