5 papers
Enhancing Transformer-Based Foundation Models for Time Series Forecasting via Bagging, Boosting and Statistical Ensembles
Dhruv D. Modi, Rong Pan
Time series foundation models (TSFMs) such as Lag-Llama, TimeGPT, Chronos, MOMENT, UniTS, and TimesFM have shown strong generalization and zero-shot capabilities for time series fo…
Human Digital Twin: Data, Models, Applications, and Challenges
Rong Pan, Hongyue Sun, Xiaoyu Chen +2
Human digital twins (HDTs) are dynamic, data-driven virtual representations of individuals, continuously updated with multimodal data to simulate, monitor, and predict health traje…
Active Learning for Multiple Change Point Detection in Non-stationary Time Series with Deep Gaussian Processes
Hao Zhao, Rong Pan
Multiple change point (MCP) detection in non-stationary time series is challenging due to the variety of underlying patterns. To address these challenges, we propose a novel algori…
Gaussian Derivative Change-point Detection for Early Warnings of Industrial System Failures
Hao Zhao, Rong Pan
An early warning of future system failure is essential for conducting predictive maintenance and enhancing system availability. This paper introduces a three-step framework for ass…
Exploring Foundation Models in Remote Sensing Image Change Detection: A Comprehensive Survey
Zihan Yu, Tianxiao Li, Yuxin Zhu +1
Change detection, as an important and widely applied technique in the field of remote sensing, aims to analyze changes in surface areas over time and has broad applications in area…