Abstract Correct and timely detection of plasma confinement regimes and edge localized modes (ELMs) is important for improving the operation of tokamaks. Existing machine learning approaches detect these regimes as a form of post-processing of experimental data. Moreover, they are typically trained on a large dataset of tens of labeled discharges, which may be costly to build. We investigate the ability of current machine learning approaches to detect the confinement regime and ELMs with the smallest possible delay after the latest measurement. We also demonstrate that including unlabeled data into the training process can improve the results in a situation where only a limited set of reliable labels is available. All training and validation is performed on data from the COMPASS tokamak. The InceptionTime architecture trained using a semi-supervised approach was found to be the most accurate method based on the set of tested variants. It is able to achieve good overall accuracy of the regime classification at the time instant of 100 µs delayed behind the latest data record. We also evaluate the capability of the model to correctly predict class transitions. While ELM occurrence can be detected with a tolerance smaller than 50 µs, detection of the confinement regime transition is more demanding and it was successful with 2 ms tolerance. Sensitivity studies to different values of model parameters are provided. We believe that the achieved accuracy is acceptable in practice and the method could be used in real-time operation.
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