Sawtooth Sampling for Time Series Denoising Diffusion Implicit Models

Heiko Oppel; Andreas Spilz; Michael Munz

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arXiv · 2511.21320

Sawtooth Sampling for Time Series Denoising Diffusion Implicit Models

Heiko Oppel·Andreas Spilz·Michael Munz

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Abstract

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.

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BibTeX RIS

Heiko Oppel, Andreas Spilz, Michael Munz. 2025-11-26. Sawtooth Sampling for Time Series Denoising Diffusion Implicit Models. https://arxiv.org/abs/2511.21320

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