Summary of Diffusion-ts: Interpretable Diffusion For General Time Series Generation, by Xinyu Yuan and Yan Qiao
Diffusion-TS: Interpretable Diffusion for General Time Series Generation
by Xinyu Yuan, Yan Qiao
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel diffusion-based framework called Diffusion-TS for generating high-quality multivariate time series samples. Building upon denoising diffusion probabilistic models (DDPMs), which have shown breakthroughs in audio synthesis and time series imputation, the authors introduce an encoder-decoder transformer with disentangled temporal representations to capture semantic meaning and sequential information. The model is trained to directly reconstruct sample noise using a Fourier-based loss term, achieving state-of-the-art results on various realistic time series analyses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This new method, called Diffusion-TS, can generate realistic time series that are both interpretable and real. It can also be easily extended to conditional generation tasks like forecasting and imputation without needing any changes to the model. The authors tested Diffusion-TS using qualitative and quantitative experiments and found it outperformed other methods. |
Keywords
* Artificial intelligence * Diffusion * Encoder decoder * Time series * Transformer
