Time series forecasting methods and their applications to particle accelerators

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 acc...

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Bibliographic Details
Published inPhysical review. Accelerators and beams Vol. 26; no. 2; p. 024801
Main Authors Li, Sichen, Adelmann, Andreas
Format Journal Article
LanguageEnglish
Published College Park American Physical Society 01.02.2023
Subjects
Forecasting
Optimization
Particle accelerators
Preempting
Structured data
Time series
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Summary: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.
ISSN:2469-9888
2469-9888
DOI:10.1103/PhysRevAccelBeams.26.024801