  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.
