DiffStyleTS: Diffusion Model for Style Transfer in Time Series
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914089870032896 |
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| author | Nagda, Mayank Ostheimer, Phil Arweiler, Justus Jungjohann, Indra Werner, Jennifer Wagner, Dennis Muraleedharan, Aparna Jafari, Pouya Schmid, Jochen Jirasek, Fabian Burger, Jakob Bortz, Michael Hasse, Hans Mandt, Stephan Kloft, Marius Fellenz, Sophie |
| author_facet | Nagda, Mayank Ostheimer, Phil Arweiler, Justus Jungjohann, Indra Werner, Jennifer Wagner, Dennis Muraleedharan, Aparna Jafari, Pouya Schmid, Jochen Jirasek, Fabian Burger, Jakob Bortz, Michael Hasse, Hans Mandt, Stephan Kloft, Marius Fellenz, Sophie |
| contents | Style transfer combines the content of one signal with the style of another. It supports applications such as data augmentation and scenario simulation, helping machine learning models generalize in data-scarce domains. While well developed in vision and language, style transfer methods for time series data remain limited. We introduce DiffTSST, a diffusion-based framework that disentangles a time series into content and style representations via convolutional encoders and recombines them through a self-supervised attention-based diffusion process. At inference, encoders extract content and style from two distinct series, enabling conditional generation of novel samples to achieve style transfer. We demonstrate both qualitatively and quantitatively that DiffTSST achieves effective style transfer. We further validate its real-world utility by showing that data augmentation with DiffTSST improves anomaly detection in data-scarce regimes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11335 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DiffStyleTS: Diffusion Model for Style Transfer in Time Series Nagda, Mayank Ostheimer, Phil Arweiler, Justus Jungjohann, Indra Werner, Jennifer Wagner, Dennis Muraleedharan, Aparna Jafari, Pouya Schmid, Jochen Jirasek, Fabian Burger, Jakob Bortz, Michael Hasse, Hans Mandt, Stephan Kloft, Marius Fellenz, Sophie Machine Learning Style transfer combines the content of one signal with the style of another. It supports applications such as data augmentation and scenario simulation, helping machine learning models generalize in data-scarce domains. While well developed in vision and language, style transfer methods for time series data remain limited. We introduce DiffTSST, a diffusion-based framework that disentangles a time series into content and style representations via convolutional encoders and recombines them through a self-supervised attention-based diffusion process. At inference, encoders extract content and style from two distinct series, enabling conditional generation of novel samples to achieve style transfer. We demonstrate both qualitatively and quantitatively that DiffTSST achieves effective style transfer. We further validate its real-world utility by showing that data augmentation with DiffTSST improves anomaly detection in data-scarce regimes. |
| title | DiffStyleTS: Diffusion Model for Style Transfer in Time Series |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.11335 |