Sawtooth Sampling for Time Series Denoising Diffusion Implicit Models

• URL: http://arxiv.org/abs/2511.21320v1
• Date: Wed, 26 Nov 2025 12:05:44 GMT
• Title: Sawtooth Sampling for Time Series Denoising Diffusion Implicit Models
• Authors: Heiko Oppel, Andreas Spilz, Michael Munz,
• Abstract summary: We combine implicit diffusion models with a novel Sawtooth Sampler that accelerates the reverse process and can be applied to any pretrained diffusion model.<n>Our approach achieves a 30 times speed-up over the standard baseline while also enhancing the quality of the generated sequences for classification tasks.
• Score: 0.9558392439655014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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