works on

From the 1 of 7 linked papers with an AI index.

most citedMantis: Lightweight Foundation Model for Time Series Classification

1 citations · 1 across the 5 of their papers we have counts for

collaborators

8 papers

cs.LG2026

ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning

Juntao Fang, Shifeng Xie, Ruichu Cai +6

Time series classification underpins applications in healthcare, sensing, and industrial monitoring. Although time series foundation models support forecasting and transferable rep…

cs.LG2026

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…

cs.LG2026

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…

cs.LG2026

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…

cs.LG20261 cited

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…

cs.LG2026

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…