7 papers
EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysis
Lei Zan, Keli Zhang, Shifeng Xie +7
EvoCause leverages a large language model to suggest edits to causal graphs used for root cause analysis of alarm cascades, refining the graph with expert labels and improving dete…
Post-Training in Time Series Foundation Models: A Unifying Framework
Shifeng Xie, Ambroise Odonnat, Zehao Xiao +7
Time series foundation models (TSFMs) have emerged as general-purpose models for time series analysis, but pretraining alone is often insufficient for reliable downstream deploymen…
Rethinking Zero-Shot Time Series Classification: From Task-specific Classifiers to In-Context Inference
Juntao Fang, Shifeng Xie, Shengbin Nie +7
The zero-shot evaluation of time series foundation models (TSFMs) for classification typically uses a frozen encoder followed by a task-specific classifier. However, this practice…
Mantis: Lightweight Foundation Model for Time Series Classification
Vasilii Feofanov, Songkang Wen, Shifeng Xie +10
While foundation models have revolutionized various domains, their application to time series classification remains rather under-explored, with existing literature predominantly f…
TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models
Hongkai Li, Shifeng Xie, Lefei Shen +7
Time series foundation models (TSFMs) are increasingly pretrained on large corpora, raising concerns that evaluation datasets may have been exposed during pretraining and thus yiel…
CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data
Shifeng Xie, Vasilii Feofanov, Ambroise Odonnat +7
Time series foundation models (TSFMs) have recently gained significant attention due to their strong zero-shot capabilities and widespread real-world applications. Such models typi…