Summary of Auto-regressive Moving Diffusion Models For Time Series Forecasting, by Jiaxin Gao et al.
Auto-Regressive Moving Diffusion Models for Time Series Forecasting
by Jiaxin Gao, Qinglong Cao, Yuntian Chen
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Applications (stat.AP)
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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 A novel Auto-Regressive Moving Diffusion (ARMD) model is proposed for time series forecasting, addressing the fundamental misalignment between traditional diffusion patterns and the time series forecasting objective. By employing chain-based diffusion with priors, the ARMD model accurately models the evolution of time series, leveraging intermediate state information to improve forecasting accuracy and stability. In contrast to previous methods that start from white Gaussian noise, the ARMD model reinterprets the diffusion process by considering future series as the initial state and historical series as the final state, with intermediate series generated using a sliding-based technique during the forward process. This design aligns the diffusion model’s sampling procedure with the forecasting objective, resulting in an unconditional, continuous sequential diffusion TSF model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series forecasting is important for many fields, like weather prediction and stock market analysis. Researchers have been working on new ways to do this, called diffusion-based models. But these models don’t always work well because they treat time series data as just random noise. This paper proposes a new approach that looks at time series as continuous patterns that evolve over time. The new model is called Auto-Regressive Moving Diffusion (ARMD). It uses a special type of diffusion that considers the whole history of the time series, not just some random starting point. This makes it better at predicting future values in the time series. The paper shows that this approach works really well on many different datasets. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Time series
