3 citations · 5 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
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…
Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes
Hao Zhao, Rong Pan
Change-point detection (CPD) is crucial for identifying abrupt shifts in data, which influence decision-making and efficient resource allocation across various domains. To address…