Initial results with time series forecasting of TJ-II heliac waveforms

G. Farias, S. Dormido-Canto, J. Vega, N. Díaz

Research output: Contribution to journalArticlepeer-review


This article discusses about how to apply forecasting techniques to predict future samples of plasma signals during a discharge. One application of the forecasting could be to detect in real time anomalous behaviors in fusion waveforms. The work describes the implementation of three prediction techniques; two of them based on machine learning methods such as artificial neural networks and support vector machines for regression. The results have shown that depending on the temporal horizon, the predictions match the real samples in most cases with an error less than 5%, even more the forecasting of five samples ahead can reach accuracy over 90% in most signals analyzed.

Original languageEnglish
Pages (from-to)777-781
Number of pages5
JournalFusion Engineering and Design
StatePublished - 1 Oct 2015


  • Artificial neural networks
  • Forecasting
  • Prediction
  • Signals
  • Support vector machines
  • Waveforms


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