Summary of Score-cdm: Score-weighted Convolutional Diffusion Model For Multivariate Time Series Imputation, by S. Zhang et al.
Score-CDM: Score-Weighted Convolutional Diffusion Model for Multivariate Time Series Imputation
by S. Zhang, S. Wang, H. Miao, H. Chen, C. Fan, J. Zhang
First submitted to arxiv on: 21 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 approach to imputing incomplete multivariant time series (MTS) data by developing a Score-weighted Convolutional Diffusion Model (Score-CDM). This model combines the strengths of convolutional neural networks (CNNs) and attention mechanisms for temporal feature learning, while adaptively trading off local and global features. The backbone consists of a Score-weighted Convolution Module (SCM) and an Adaptive Reception Module (ARM), which utilizes Fast Fourier Transformation to balance local and global features. The authors evaluate their approach on three real MTS datasets and demonstrate its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to solve a big problem in dealing with incomplete time series data. It creates a new way to fill in the missing pieces using a special kind of model called Score-CDM. This model is good at combining different kinds of information from the past to help predict what might happen next. The authors test their idea on some real datasets and show that it works really well. |
Keywords
» Artificial intelligence » Attention » Diffusion model » Time series
