7 papers
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…
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…
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…
Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective
Chengsen Wang, Qi Qi, Jingyu Wang +4
Time series forecasting has played a pivotal role across various industries, including finance, transportation, energy, healthcare, and climate. Due to the abundant seasonal inform…
Understanding and Guiding Weakly Supervised Entity Alignment with Potential Isomorphism Propagation
Yuanyi Wang, Wei Tang, Haifeng Sun +5
Weakly Supervised Entity Alignment (EA) is the task of identifying equivalent entities across diverse knowledge graphs (KGs) using only a limited number of seed alignments. Despite…