# Review of Time Series Forecasting Methods and Their Applications to Particle Accelerators

arXiv: https://arxiv.org/abs/2209.10705 · HTML: https://mlsanity.com/paper/2209.10705/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 101 references that are in the map.
- First posted on arXiv 21 Sep 2022.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions](https://mlsanity.com/paper/1909.00590/) (2019)
- [Machine Learning Advances for Time Series Forecasting](https://mlsanity.com/paper/2012.12802/) (2020)
- [A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators](https://mlsanity.com/paper/2102.00786/) (2021)
- [BOOSTR: A Dataset for Accelerator Control Systems](https://mlsanity.com/paper/2101.08359/) (2021)

## Built on by (most cited first, 3 of 3)

- [Accelerating Cavity Fault Prediction Using Deep Learning at Jefferson Laboratory](https://mlsanity.com/paper/2404.15829/) (2024)
- [Forecasting Particle Accelerator Interruptions Using Logistic LASSO Regression](https://mlsanity.com/paper/2303.08984/) (2023)
- [Seasonal and Quantum-inspired Models for Neutron Monitor Time Series Forecasting](https://mlsanity.com/paper/2609.30281/) (2026)

## Abstract (arXiv)

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.