Review of Time Series Forecasting Methods and Their Applications to Particle Accelerators
It addresses optimization and control challenges for particle accelerator facilities, but is incremental as it reviews and applies existing methods.
This review tackles the problem of applying time series forecasting methods to optimize particle accelerators, summarizing existing models and highlighting encouraging results and ongoing efforts to address issues like data consistency.
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 pre-emptive 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 the promise for broader use, and existing problems such as data consistency and compatibility have started to be addressed.