Summary of Latent Space Score-based Diffusion Model For Probabilistic Multivariate Time Series Imputation, by Guojun Liang et al.
Latent Space Score-based Diffusion Model for Probabilistic Multivariate Time Series Imputation
by Guojun Liang, Najmeh Abiri, Atiye Sadat Hashemi, Jens Lundström, Stefan Byttner, Prayag Tiwari
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 proposed Latent Space Score-Based Diffusion Model (LSSDM) aims to improve probabilistic multivariate time series imputation by leveraging the latent distribution in a lower-dimensional space. The model first projects observed values onto this latent space, then reconstructs coarse missing data without labels using unsupervised learning. Finally, it uses a conditional diffusion model to obtain precise imputed values and assess uncertainty. LSSDM outperforms previous methods in terms of imputation performance while providing better explanations and uncertainty analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to fill in gaps in time series data, like the ones used in weather forecasting or finance. Instead of just guessing what might be there, this model tries to understand the underlying pattern in the data and use that to make more accurate predictions. It works by first reducing the dimensionality of the data to find the main patterns, then using that information to fill in the gaps. The result is a more accurate and reliable way to work with time series data. |
Keywords
» Artificial intelligence » Diffusion model » Latent space » Time series » Unsupervised
