@inproceedings{04f7e8356b4d475ca1aee54b85945b5e,
title = "Applying forecasting to fusion databases",
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.",
keywords = "ARIMA, Forecasting, SVR, Signals",
author = "Gonzalo Farias and Sebasti{\'a}n Dormido-Canto and Jes{\'u}s Vega and Norman D{\'i}az",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 3rd International Symposium on Statistical Learning and Data Sciences, SLDS 2015 ; Conference date: 20-04-2015 Through 23-04-2015",
year = "2015",
doi = "10.1007/978-3-319-17091-6_30",
language = "English",
isbn = "9783319170909",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "356--365",
editor = "Alexander Gammerman and Vladimir Vovk and Harris Papadopoulos",
booktitle = "Statistical Learning and Data Sciences - 3rd International Symposium, SLDS 2015, Proceedings",
}