Mantis: Lightweight Calibrated Foundation Model for User-Friendly Time Series Classification

• URL: http://arxiv.org/abs/2502.15637v1
• Date: Fri, 21 Feb 2025 18:06:09 GMT
• Title: Mantis: Lightweight Calibrated Foundation Model for User-Friendly Time Series Classification
• Authors: Vasilii Feofanov, Songkang Wen, Marius Alonso, Romain Ilbert, Hongbo Guo, Malik Tiomoko, Lujia Pan, Jianfeng Zhang, Ievgen Redko,
• Abstract summary: We present Mantis, a new open-source foundation model for time series classification based on the Vision Transformer architecture.<n>Our experimental results show that Mantis outperforms existing foundation models both when the backbone is frozen and when fine-tuned.
• Score: 16.738168952631735
• License: http://creativecommons.org/licenses/by/4.0/
• 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. In addition, we propose several adapters to handle the multivariate setting, reducing memory requirements and modeling channel interdependence.

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