6 papers
WaveMoE: A Wavelet-Enhanced Mixture-of-Experts Foundation Model for Time Series Forecasting
Shunyu Wu, Jiawei Huang, Weibin Feng +6
Time series foundation models (TSFMs) have recently achieved remarkable success in universal forecasting by leveraging large-scale pretraining on diverse time series data. Compleme…
Lightweight Time Series Data Valuation on Time Series Foundation Models via In-Context Finetuning
Shunyu Wu, Tianyue Li, Yixuan Leng +4
Time series foundation models (TSFMs) have demonstrated increasing capabilities due to their extensive pretraining on large volumes of diverse time series data. Consequently, the q…
Integrating Time Series into LLMs via Multi-layer Steerable Embedding Fusion for Enhanced Forecasting
Zhuomin Chen, Dan Li, Jiahui Zhou +4
Time series (TS) data are ubiquitous across various application areas, rendering time series forecasting (TSF) a fundamental task. With the astounding advances in large language mo…
Rating Quality of Diverse Time Series Data by Meta-learning from LLM Judgment
Shunyu Wu, Dan Li, Wenjie Feng +3
High-quality time series (TS) data are essential for ensuring TS model performance, rendering research on rating TS data quality indispensable. Existing methods have shown promisin…
Enhancing LLM Reasoning for Time Series Classification by Tailored Thinking and Fused Decision
Jiahui Zhou, Dan Li, Lin Li +5
The reasoning capabilities of large language models (LLMs) have significantly advanced their performance by enabling in-depth understanding of diverse tasks. With growing interest…
BACE-RUL: A Bi-directional Adversarial Network with Covariate Encoding for Machine Remaining Useful Life Prediction
Zekai Zhang, Dan Li, Shunyu Wu +4
Prognostic and Health Management (PHM) are crucial ways to avoid unnecessary maintenance for Cyber-Physical Systems (CPS) and improve system reliability. Predicting the Remaining U…