Abstract
Historical Solar X-rays time series are employed to track solar activity and solar flares. High level of X-rays released during Solar Flares can interfere in telecommunication equipment operation. In this sense, it is important the development of computational methods to forecast Solar Flares analyzing the X-ray emissions. In this work, historical Solar X-rays time series sequences are employed to predict future Solar Flares using traditional classification algorithms. However, for large data sequences, the classification algorithms face the problem of “dimensionality curse”, where the algorithms performance and accuracy degrade with the increase in the sequence size. To deal with this problem, we proposed a method that employs feature selection to determine which time instants of a sequence should be considered by the mining process, reducing the processing time and increasing the accuracy of the mining process. Moreover, the proposed method also determines which are the antecedent time instants that most affect a future Solar Flare.
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Junior, S.L.D., Cecatto, J.R., Fernandes, M.M., Ribeiro, M.X. (2018). An Optimized Data Mining Method to Support Solar Flare Forecast. In: Latifi, S. (eds) Information Technology - New Generations. Advances in Intelligent Systems and Computing, vol 558. Springer, Cham. https://doi.org/10.1007/978-3-319-54978-1_60
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DOI: https://doi.org/10.1007/978-3-319-54978-1_60
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