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[2602.17868] MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategies
Summary
MantisV2 introduces advanced techniques for zero-shot time series classification, utilizing synthetic data and refined test-time strategies to enhance model performance.
Why It Matters
This research addresses the significant performance gap in time series classification models, providing innovative solutions that leverage synthetic data and advanced methodologies. The findings have implications for various applications in machine learning, particularly in fields requiring efficient and effective data processing.
Key Takeaways
- MantisV2 and Mantis+ improve zero-shot feature extraction for time series.
- The models utilize synthetic data for pre-training, enhancing performance.
- Refined test-time strategies lead to better output-token aggregation.
- Self-ensembling and cross-model embedding fusion further boost results.
- Extensive experiments demonstrate state-of-the-art performance on key benchmarks.
Computer Science > Machine Learning arXiv:2602.17868 (cs) [Submitted on 19 Feb 2026] Title:MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategies Authors:Vasilii Feofanov, Songkang Wen, Jianfeng Zhang, Lujia Pan, Ievgen Redko View a PDF of the paper titled MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategies, by Vasilii Feofanov and 4 other authors View PDF HTML (experimental) Abstract: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. Although early models such as Mantis have shown the promise of this approach, a substantial performance gap remained between frozen and fine-tuned encoders. In this work, we introduce methods that significantly strengthen zero-shot feature extraction for time series. First, we introduce Mantis+, a variant of Mantis pre-trained entirely on synthetic time series. Second, through controlled ablation studies, we refine the architecture and obtain MantisV2, an improved and more lightweight encoder. Third, we propose an enhanced test-time methodology that leverages intermediate-layer representations and refines output-token aggregation. In addition, we show that performance can be further improved via self-ensembling and cross-model embedding fusion. Extensive experiments on UCR, UEA, Human Activity Recognitio...