In recent years, there has been increasing interest in developing foundation models for time series data that can generalize across diverse downstream tasks. While numerous forecasting-oriented foundation models have been introduced, there is a notable scarcity of models tailored for time series classification. To address this gap, we present Mantis, a new open-source foundation model for time series classification based on the Vision Transformer (ViT) architecture that has been pre-trained using a contrastive learning approach. Our experimental results show that Mantis outperforms existing foundation models both when the backbone is frozen and when fine-tuned, while achieving the lowest calibration error. In addition, we propose several adapters to handle the multivariate setting, reducing memory requirements and modeling channel interdependence.
@article{arxiv.2502.15637,
title = {Mantis: Lightweight Calibrated Foundation Model for User-Friendly Time Series Classification},
author = {Vasilii Feofanov and Songkang Wen and Marius Alonso and Romain Ilbert and Hongbo Guo and Malik Tiomoko and Lujia Pan and Jianfeng Zhang and Ievgen Redko},
journal= {arXiv preprint arXiv:2502.15637},
year = {2025}
}