4 papers
ChronoSteer: Bridging Large Language Model and Time Series Foundation Model via Synthetic Data
Chengsen Wang, Qi Qi, Zhongwen Rao +3
Conventional forecasting methods rely on unimodal time series data, limiting their ability to exploit rich textual information. Recently, large language models (LLMs) and time seri…
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…
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…
ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Data
Chengsen Wang, Qi Qi, Jingyu Wang +5
Human experts typically integrate numerical and textual multimodal information to analyze time series. However, most traditional deep learning predictors rely solely on unimodal nu…