Abstract
This manuscript describes the application of four forecasting methods to predict future magnitudes of plasma signals during the discharge. One application of the forecasting could be to provide in advance signal magnitudes in order to detect in real-time previously known patterns such as plasma instabilities. The forecasting was implemented for four different prediction techniques from classical and machine learning approaches. The results show that the performance of predictions can get a high level of accuracy and precision. In fact, over 95 % of predictions match the real magnitudes in most signals.
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Farias, G., Dormido-Canto, S., Vega, J., Díaz, N. (2015). Applying Forecasting to Fusion Databases. In: Gammerman, A., Vovk, V., Papadopoulos, H. (eds) Statistical Learning and Data Sciences. SLDS 2015. Lecture Notes in Computer Science(), vol 9047. Springer, Cham. https://doi.org/10.1007/978-3-319-17091-6_30
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DOI: https://doi.org/10.1007/978-3-319-17091-6_30
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