Time series forecasting methods and their applications to particle accelerators
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Author / Producer
Date
2023-02
Publication Type
Review Article
ETH Bibliography
yes
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Abstract
Particle accelerators are complex facilities that produce large amounts of structured data and have clear optimization goals as well as precisely defined control requirements. As such they are naturally amenable to data-driven research methodologies. The data from sensors and monitors inside the accelerator form multivariate time series. With fast preemptive approaches being highly preferred in accelerator control and diagnostics, the application of data-driven time series forecasting methods is particularly promising. This review formulates the time series forecasting problem and summarizes existing models with applications in various scientific areas. Several current and future attempts in the field of particle accelerators are introduced. The application of time series forecasting to particle accelerators has shown encouraging results and promise for broader use, and existing problems such as data consistency and compatibility have started to be addressed.
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Publication status
published
Editor
Book title
Journal / series
Volume
26 (2)
Pages / Article No.
24801
Publisher
American Physical Society