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

Information

Marcela Xavier Ribeiro¹  Márcio Merino Fernandes¹  Sérgio Luisir Díscola Junior¹  José Roberto Cecatto² 

[1] Department of Computer Science, Federal University of São Carlos (UFSCar) , São Carlos 13565-905, Brazil;National Institute for Space Research (INPE), São José dos Campos 12227-010, Brazil;

关键词: solar flare;    X-rays;    k-nearest neighbour classifier;    sliding window;    forecasting;    time series;    data mining;    feature selection;    graphical processing unit (GPU);    CUDA;   

DOI  :  10.3390/info9010008

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown