Applying forecasting to fusion databases

GONZALO ALBERTO FARIAS CASTRO, Sebastián Dormido-Canto, Jesús Vega, Norman Díaz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationStatistical Learning and Data Sciences - 3rd International Symposium, SLDS 2015, Proceedings
EditorsAlexander Gammerman, Vladimir Vovk, Harris Papadopoulos
PublisherSpringer Verlag
Pages356-365
Number of pages10
ISBN (Print)9783319170909
DOIs
StatePublished - 1 Jan 2015
Event3rd International Symposium on Statistical Learning and Data Sciences, SLDS 2015 - Egham, United Kingdom
Duration: 20 Apr 201523 Apr 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9047
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Symposium on Statistical Learning and Data Sciences, SLDS 2015
CountryUnited Kingdom
CityEgham
Period20/04/1523/04/15

Keywords

  • ARIMA
  • Forecasting
  • Signals
  • SVR

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