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Information 2018, 9(1), 8; doi:10.3390/info9010008

SeMiner: A Flexible Sequence Miner Method to Forecast Solar Time Series

1
Department of Computer Science, Federal University of São Carlos (UFSCar) , São Carlos 13565-905, Brazil
2
Federal Institute of Education, Science and Technology of São Paulo (IFSP), São Carlos 13565-905, Brazil
3
National Institute for Space Research (INPE), São José dos Campos 12227-010, Brazil
Current address: Federal University of São Carlos, Rodovia Washington Luis, km 235, São Carlos 13565-905, Brazil
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 12 December 2017 / Revised: 29 December 2017 / Accepted: 2 January 2018 / Published: 4 January 2018
(This article belongs to the Special Issue Information Technology: New Generations (ITNG 2017))
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Abstract

X-rays emitted by the Sun can damage electronic devices of spaceships, satellites, positioning systems and electricity distribution grids. Thus, the forecasting of solar X-rays is needed to warn organizations and mitigate undesirable effects. Traditional mining classification methods categorize observations into labels, and we aim to extend this approach to predict future X-ray levels. Therefore, we developed the “SeMiner” method, which allows the prediction of future events. “SeMiner” processes X-rays into sequences employing a new algorithm called “Series-to-Sequence” (SS). It employs a sliding window approach configured by a specialist. Then, the sequences are submitted to a classifier to generate a model that predicts X-ray levels. An optimized version of “SS” was also developed using parallelization techniques and Graphical Processing Units, in order to speed up the entire forecasting process. The obtained results indicate that “SeMiner” is well-suited to predict solar X-rays and solar flares within the defined time range. It reached more than 90% of accuracy for a 2-day forecast, and more than 80% of True Positive (TPR) and True Negative (TNR) rates predicting X-ray levels. It also reached an accuracy of 72.7%, with a TPR of 70.9% and TNR of 79.7% when predicting solar flares. Moreover, the optimized version of “SS” proved to be 4.36 faster than its initial version. View Full-Text
Keywords: solar flare; X-rays; k-nearest neighbour classifier; sliding window; forecasting; time series; data mining; feature selection; graphical processing unit (GPU); CUDA solar flare; X-rays; k-nearest neighbour classifier; sliding window; forecasting; time series; data mining; feature selection; graphical processing unit (GPU); CUDA
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Díscola Junior, S.L.; Cecatto, J.R.; Merino Fernandes, M.; Xavier Ribeiro, M. SeMiner: A Flexible Sequence Miner Method to Forecast Solar Time Series. Information 2018, 9, 8.

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