@Article{DíscolaJúniorCecaFernRibe:2018:FlSeMi,
               author = "D{\'{\i}}scola J{\'u}nior, S{\'e}rgio Luisir and Cecatto, 
                         Jos{\'e} Roberto and Fernandes, M{\'a}rcio Merino and Ribeiro, 
                         Marcela Xavier",
          affiliation = "{Universidade Federal de S{\~a}o Carlos (UFCar)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         de S{\~a}o Carlos (UFCar)} and {Universidade Federal de S{\~a}o 
                         Carlos (UFCar)}",
                title = "SeMiner: a flexible sequence miner method to forecast solar time 
                         series",
              journal = "Information (Switzerland)",
                 year = "2018",
               volume = "9",
               number = "1",
                pages = "e8",
                month = "jan.",
             keywords = "solar flare, X-rays, k-nearest neighbour classifier, sliding 
                         window, forecasting, time series, data mining, feature selection, 
                         graphical processing unit (GPU), CUDA.",
             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.",
                  doi = "10.3390/info9010008",
                  url = "http://dx.doi.org/10.3390/info9010008",
                 issn = "2078-2489",
             language = "en",
           targetfile = "discola_seminer.pdf",
        urlaccessdate = "27 abr. 2024"
}