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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2506.05276 |
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| _version_ | 1866916781223837696 |
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| author | Yu, Hao Cheng, Chu Xin Yu, Runlong Ye, Yuyang Tong, Shiwei Liu, Zhaofeng Lian, Defu |
| author_facet | Yu, Hao Cheng, Chu Xin Yu, Runlong Ye, Yuyang Tong, Shiwei Liu, Zhaofeng Lian, Defu |
| contents | Recent advances in time series generation have shown promise, yet controlling properties in generated sequences remains challenging. Time Series Editing (TSE) - making precise modifications while preserving temporal coherence - consider both point-level constraints and segment-level controls that current methods struggle to provide. We introduce the CocktailEdit framework to enable simultaneous, flexible control across different types of constraints. This framework combines two key mechanisms: a confidence-weighted anchor control for point-wise constraints and a classifier-based control for managing statistical properties such as sums and averages over segments. Our methods achieve precise local control during the denoising inference stage while maintaining temporal coherence and integrating seamlessly, with any conditionally trained diffusion-based time series models. Extensive experiments across diverse datasets and models demonstrate its effectiveness. Our work bridges the gap between pure generative modeling and real-world time series editing needs, offering a flexible solution for human-in-the-loop time series generation and editing. The code and demo are provided for validation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05276 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How to Unlock Time Series Editing? Diffusion-Driven Approach with Multi-Grained Control Yu, Hao Cheng, Chu Xin Yu, Runlong Ye, Yuyang Tong, Shiwei Liu, Zhaofeng Lian, Defu Machine Learning Recent advances in time series generation have shown promise, yet controlling properties in generated sequences remains challenging. Time Series Editing (TSE) - making precise modifications while preserving temporal coherence - consider both point-level constraints and segment-level controls that current methods struggle to provide. We introduce the CocktailEdit framework to enable simultaneous, flexible control across different types of constraints. This framework combines two key mechanisms: a confidence-weighted anchor control for point-wise constraints and a classifier-based control for managing statistical properties such as sums and averages over segments. Our methods achieve precise local control during the denoising inference stage while maintaining temporal coherence and integrating seamlessly, with any conditionally trained diffusion-based time series models. Extensive experiments across diverse datasets and models demonstrate its effectiveness. Our work bridges the gap between pure generative modeling and real-world time series editing needs, offering a flexible solution for human-in-the-loop time series generation and editing. The code and demo are provided for validation. |
| title | How to Unlock Time Series Editing? Diffusion-Driven Approach with Multi-Grained Control |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.05276 |