{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mantis-lightweight-calibrated-foundation","title":"Mantis: Lightweight Calibrated Foundation Model for User-Friendly Time Series Classification","arxiv_id":"2502.15637","date":"2025-02-21","proceeding":null,"authors":["Vasilii Feofanov","Songkang Wen","Marius Alonso","Romain Ilbert","Hongbo Guo","Malik Tiomoko","Lujia Pan","Jianfeng Zhang","Ievgen Redko"],"abstract":"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. 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