3 papers
cs.LG2026
ECHO: Correcting Time Series Foundation Model Forecasts with Historical Analogues and Errors
Boya Zhang, Xilin Dai, Yilin Wang +4
Time Series Foundation Models (TSFMs) provide general-purpose forecasting capabilities, yet their forecasts can remain misaligned with the temporal characteristics of a target doma…
cs.LG2026
FreqCycle: A Multi-Scale Time-Frequency Analysis Method for Time Series Forecasting
Boya Zhang, Shuaijie Yin, Huiwen Zhu +1
Mining time-frequency features is critical for time series forecasting. Existing research has predominantly focused on modeling low-frequency patterns, where most time series energ…
cs.LG2025
Hierarchical Sparse Bayesian Multitask Learning for Disease Prediction in Pooled Microbiome Studies
Haonan Zhu, Andre R. Goncalves, Camilo Valdes +10
This paper proposes a hierarchical Bayesian multitask learning model that is applicable to the general multi-task binary classification learning problem where the model assumes a s…