@InProceedings{CruzKayCalGarQui:2023:EvTiSe,
               author = "Cruz, Juliano Elias Cardoso and Kayano, Mary Toshie and Calheiros, 
                         Alan James Peixoto and Garcia, S{\^a}mia R. and Quiles, Marcos 
                         G.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Universidade Federal de S{\~a}o 
                         Paulo (UNIFESP)} and {Universidade Federal de S{\~a}o Paulo 
                         (UNIFESP)}",
                title = "Evaluation of Time Series Causal Detection Methods on the 
                         Influence of Pacific and Atlantic Ocean over Northeastern Brazil 
                         Precipitation",
            booktitle = "Proceedings...",
                 year = "2023",
                pages = "e297249",
         organization = "International Conference on Computational Science and Its 
                         Applications, 23.",
            publisher = "Springer",
             keywords = "causality, ENSO, precipitation, time series.",
             abstract = "The detection of causation in natural systems or phenomena has 
                         been a fundamental task of science for a long time. In recent 
                         decades, data-driven approaches have emerged to perform this task 
                         automatically. Some of them are specialized in time series. 
                         However, there is no clarity in literature what methods perform 
                         better in what scenarios. Thus this paper presents an evaluation 
                         of causality detection methods for time series using a well-known 
                         and extensively studied case study: the influence of El 
                         Niņo-Southern Oscillation and Intertropical Convergence Zone on 
                         precipitation in Northeastern Brazil. We employed multiple 
                         approaches and two datasets to evaluate the methods, and found 
                         that the SELVAR and SLARAC methods delivered the best 
                         performance.",
  conference-location = "Athens",
      conference-year = "03-06 July 2023",
                  doi = "10.1007/978-3-031-36805-9_28",
                  url = "http://dx.doi.org/10.1007/978-3-031-36805-9_28",
                 isbn = "978-303136804-2",
                 issn = "03029743",
             language = "en",
        urlaccessdate = "08 maio 2024"
}