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12 papers
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…
LLM Pretraining Shapes a Generalizable Manifold: Insights into Cross-Modal Transfer to Time Series
Alexis Roger, Prateek Humane, Zhenghan Tai +4
Can language-pretrained transformers become effective time-series forecasters, and why? In this paper, we show that cross-modal transfer arises because language pretraining precond…
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…
Layer by layer, module by module: Choose both for optimal OOD probing of ViT
Ambroise Odonnat, Vasilii Feofanov, Laetitia Chapel +2
Recent studies have observed that intermediate layers of foundation models often yield more discriminative representations than the final layer. While initially attributed to autor…
UTICA: Multi-Objective Self-Distllation Foundation Model Pretraining for Time Series Classification
Yessin Moakher, Youssef Attia El Hili, Vasilii Feofanov
Self-supervised foundation models have achieved remarkable success across domains, including time series. However, the potential of non-contrastive methods, a paradigm that has dri…
MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategies
Vasilii Feofanov, Songkang Wen, Jianfeng Zhang +2
Developing foundation models for time series classification is of high practical relevance, as such models can serve as universal feature extractors for diverse downstream tasks. A…