# A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators Source: [A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators]() Collection index: [llms.txt]() Data updated: 2025-10-25T03:51:02.518Z Authors: Sichen Li, M\\'elissa Zacharias, Jochem Snuverink, Jaime Coello de Portugal, Fernando Perez-Cruz, Davide Reggiani, Andreas Adelmann Published: 2021-02-01T11:53:14.000Z ## Abstract The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam time loss in the High Intensity Proton Accelerator complex by forecasting interlock events. The forecasting is performed through binary classification of windows of multivariate time series. The time series are transformed into Recurrence Plots which are then classified by a Convolutional Neural Network, which not only captures the inner structure of the time series but also utilizes the advances of image classification techniques. Our best performing interlock-to-stable classifier reaches an Area under the ROC Curve value of $0.71 \\pm 0.01$ compared to $0.65 \\pm 0.01$ of a Random Forest model, and it can potentially reduce the beam time loss by $0.5 \\pm 0.2$ seconds per interlock. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()