Sawtooth Sampling for Time Series Denoising Diffusion Implicit Models
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908677054660608 |
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| author | Oppel, Heiko Spilz, Andreas Munz, Michael |
| author_facet | Oppel, Heiko Spilz, Andreas Munz, Michael |
| contents | Denoising Diffusion Probabilistic Models (DDPMs) can generate synthetic timeseries data to help improve the performance of a classifier, but their sampling process is computationally expensive. We address this by combining implicit diffusion models with a novel Sawtooth Sampler that accelerates the reverse process and can be applied to any pretrained diffusion model. Our approach achieves a 30 times speed-up over the standard baseline while also enhancing the quality of the generated sequences for classification tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21320 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sawtooth Sampling for Time Series Denoising Diffusion Implicit Models Oppel, Heiko Spilz, Andreas Munz, Michael Machine Learning Denoising Diffusion Probabilistic Models (DDPMs) can generate synthetic timeseries data to help improve the performance of a classifier, but their sampling process is computationally expensive. We address this by combining implicit diffusion models with a novel Sawtooth Sampler that accelerates the reverse process and can be applied to any pretrained diffusion model. Our approach achieves a 30 times speed-up over the standard baseline while also enhancing the quality of the generated sequences for classification tasks. |
| title | Sawtooth Sampling for Time Series Denoising Diffusion Implicit Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.21320 |