arXiv:2502.15637v2 Announce Type: replace Abstract: While foundation models have revolutionized various domains, their application to time series classification remains rather under-explored, with existing literature predominantly focused on forecasting. To bridge this gap, we introduce \textbf{Mantis}, a transformer-based foundation model pre-trained exclusively on synthetic data via self-supervised contrastive learning. We demonstrate that effective tokenization is critical to unlocking the full potential of transformers, proposing a novel token generator unit. Furthermore, we introduce an e
The proliferation of foundation models in other AI domains is naturally leading to their exploration and application in time series analysis, an area previously dominated by forecasting.
This development addresses a critical gap in applying powerful foundation models to time series classification, opening new avenues for efficiency and accuracy across many industries that rely on time-dependent data.
The explicit focus on a lightweight, transformer-based foundation model trained on synthetic data for time series classification represents a significant methodological shift, potentially accelerating adoption beyond specialized forecasting applications.
- · AI/ML researchers
- · Data scientists
- · Industries with complex time series data (e.g., finance, healthcare, manufacturi
- · Specialized AI platform providers
- · Traditional time series analysis methods
- · Less efficient or specialized time series models
- · Companies slow to adopt advanced AI techniques
Mantis provides a more accessible and efficient foundation model for time series classification.
This could lead to a rapid expansion of AI applications in areas like predictive maintenance, anomaly detection, and real-time operational optimization across various sectors.
The success of synthetic data pre-training and novel tokenization could inspire similar lightweight foundation models for other niche data types, further democratizing advanced AI tools.
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