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cs.LG2025
ChronoSteer: Bridging Large Language Model and Time Series Foundation Model via Synthetic Cross-Modal Alignment Dataset
Chengsen Wang, Qi Qi, Zhongwen Rao +3
Conventional forecasting methods are trained end-to-end on unimodal time series, which limits their ability to exploit textual information and undermines their generalization in da…
cs.LG2025
Unlocking the Potential of Linear Networks for Irregular Multivariate Time Series Forecasting
Chengsen Wang, Qi Qi, Jingyu Wang +3
Time series forecasting holds significant importance across various industries, including finance, transportation, energy, healthcare, and climate. Despite the widespread use of li…
cs.LG2025
OIPR: Evaluation for Time-series Anomaly Detection Inspired by Operator Interest
Yuhan Jing, Jingyu Wang, Lei Zhang +6
With the growing adoption of time-series anomaly detection (TAD) technology, numerous studies have employed deep learning-based detectors to analyze time-series data in the fields…