首页|Diffusion Models for Time Series Applications: A Survey
Diffusion Models for Time Series Applications: A Survey
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Arxiv
Diffusion models, a family of generative models based on deep learning, have
become increasingly prominent in cutting-edge machine learning research. With a
distinguished performance in generating samples that resemble the observed
data, diffusion models are widely used in image, video, and text synthesis
nowadays. In recent years, the concept of diffusion has been extended to time
series applications, and many powerful models have been developed. Considering
the deficiency of a methodical summary and discourse on these models, we
provide this survey as an elementary resource for new researchers in this area
and also an inspiration to motivate future research. For better understanding,
we include an introduction about the basics of diffusion models. Except for
this, we primarily focus on diffusion-based methods for time series
forecasting, imputation, and generation, and present them respectively in three
individual sections. We also compare different methods for the same application
and highlight their connections if applicable. Lastly, we conclude the common
limitation of diffusion-based methods and highlight potential future research
directions.
Arxiv
