9 papers
Instance-Adaptive Prompts as Context for Time-Series Foundation Models
Zehao Xiao, Shifeng Xie, Lei Zan +5
Longer histories can improve time-series foundation models (TSFMs), but require substantially higher inference cost. We therefore ask whether contextual information can be provided…
Tabby: An Open Pretraining Recipe for Time Series Foundation Models
Shifeng Xie, Bahaeddine Abdessalem, Zehao Xiao +9
In this report, we release Tabby, a long context probabilistic time series foundation model, together with a complete and open recipe of how it was built. Tabby adopts an encoder-o…
FlowTSFM: Turning Encoder Depth into Quantile Transport
Bahaeddine Abdessalem, Shifeng Xie, Zehao Xiao +6
Encoder-based time series foundation models (TSFMs) typically rely on deep stacks of independently parameterized Transformer layers, where only the final forecast is supervised and…
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…
EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysis
Lei Zan, Keli Zhang, Shifeng Xie +7
Modern telecommunication, cloud, and microservice systems emit correlated alarm cascades when components fail. Root cause analysis (RCA) aims to identify the small set of alarms th…
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…
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…
CAMA: Enhancing Mathematical Reasoning in Large Language Models with Causal Knowledge
Lei Zan, Keli Zhang, Ruichu Cai +1
Large Language Models (LLMs) have demonstrated strong performance across a wide range of tasks, yet they still struggle with complex mathematical reasoning, a challenge fundamental…