Summary of A Survey on Diffusion Models For Time Series and Spatio-temporal Data, by Yiyuan Yang et al.
A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
by Yiyuan Yang, Ming Jin, Haomin Wen, Chaoli Zhang, Yuxuan Liang, Lintao Ma, Yi Wang, Chenghao Liu, Bin Yang, Zenglin Xu, Jiang Bian, Shirui Pan, Qingsong Wen
First submitted to arxiv on: 29 Apr 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 reviews the application of diffusion models in time series and spatio-temporal data mining. It categorizes diffusion models into unconditioned and conditioned types, discussing their use in predictive and generative tasks such as forecasting, anomaly detection, classification, and imputation. The survey covers the application of these models in various fields, including healthcare, recommendation, climate, energy, audio, and transportation. It provides a comprehensive understanding of how diffusion models analyze and generate data, aiming to direct future innovations and applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how diffusion models are used to understand trends and patterns over time and space. These models help us make predictions and detect anomalies in things like stock prices, weather forecasts, and medical test results. The study reviews different types of diffusion models and shows how they’re used in various fields like healthcare, music, and transportation. |
Keywords
» Artificial intelligence » Anomaly detection » Classification » Diffusion » Time series
