<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0379-3982</journal-id>
<journal-title><![CDATA[Revista Tecnología en Marcha]]></journal-title>
<abbrev-journal-title><![CDATA[Tecnología en Marcha]]></abbrev-journal-title>
<issn>0379-3982</issn>
<publisher>
<publisher-name><![CDATA[Instituto Tecnológico de Costa Rica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0379-39822018000100098</article-id>
<article-id pub-id-type="doi">10.18845/tm.v31i1.3500</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Aplicación de métodos agregados en la detección de puntos atípicos en series de tiempo meteorológicas]]></article-title>
<article-title xml:lang="en"><![CDATA[Application of ensemble methods in outlier point detection in meteorological time series]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Calvo-Valverde]]></surname>
<given-names><![CDATA[Luis-Alexander]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Acuña-Alpízar]]></surname>
<given-names><![CDATA[Nelson José]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Tecnológico de Costa Rica  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Tecnológico de Costa Rica  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2018</year>
</pub-date>
<volume>31</volume>
<numero>1</numero>
<fpage>98</fpage>
<lpage>109</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S0379-39822018000100098&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S0379-39822018000100098&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S0379-39822018000100098&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen [14] Para este trabajo de investigación, se estudió el desempeño de los métodos agregados en la detección de valores atípicos punto en series temporales uni-variables meteorológicas, utilizando la métrica F1 como medida de desempeño. Para esto se creó un programa que permite aplicar 3 clasificadores no agregados (regresión de soporte vectorial, ARIMA, redes bayesianas) y 3 clasificadores agregados (apilamiento, bagging y AdaBoost) a 3 conjuntos de datos de mediciones meteorológicas (precipitación, temperatura máxima y radiación solar). [15] Usando esta aplicación, se ejecutó un diseño experimental para comparar los clasificadores. En este diseño, primero se obtuvo el promedio de F1 de los clasificadores realizando múltiples pruebas en cada conjunto de datos. Luego, mediante una prueba estadística de hipótesis se compararon los promedios obtenidos por los clasificadores para determinar si las diferencias observadas eran significativas. Finalmente, se realizó un análisis de los resultados, enfocado en comparar el desempeño de los clasificadores agregados contra el desempeño del mejor clasificador no agregado en cada conjunto de datos. [16] En general se encontró que es posible mejorar significativamente el desempeño al detectar valores atípicos punto en algunas series temporales uni-variables utilizando métodos agregados. Sin embargo, para lograr esta mejora se deben reunir condiciones que, aunque varían dependiendo del método agregado, en general apuntan a mejorar la diversidad de los clasificadores base. Cuando no se reúnen estas condiciones, los métodos agregados no tuvieron una diferencia significativa en el desempeño con respecto al algoritmo no agregado que obtuvo el mejor desempeño en el conjunto de datos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract [20] For this research work, the performance of ensemble methods in the task of outlier points detection in meteorological univariate time series was studied, using the F1 metric to measure the performance. For this purpose, an application was created that allows applying 3 nonensemble classifiers (support vector regression, ARIMA, bayesian networks) and 3 ensemble classifiers (stacking, bagging and AdaBoost) to 3 meteorological datasets (rainfall, maximum temperature and solar radiation). [21] Using this application, an experiment was executed to compare the different classifiers. In this experiment, first, the F1 average of the algorithms was obtained by executing multiple tests in each dataset. Then, using a statistical hypothesis test we compared the obtained averages to find out if the observed differences were significant. Finally, a result analysis was performed, focused on comparing the performance of the ensemble classifiers versus the performance of the best non-ensemble classifier for each dataset. [22] In general the results indicate that it is possible to significantly improve the performance in the outlier point detection task in some uni-variate time series by using ensemble methods. However, to obtain this improvement several conditions must be met. Although the conditions vary depending on the ensemble method, in general these conditions aim to improve the diversity in the base classifiers. When these conditions were not met, the ensemble methods didn&#8217;t have a significant difference in the performance compared to the non-ensemble classifier that got the best performance in the datasets.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Valores atípicos]]></kwd>
<kwd lng="es"><![CDATA[Métodos agregados]]></kwd>
<kwd lng="es"><![CDATA[ARIMA]]></kwd>
<kwd lng="es"><![CDATA[Regresión de soporte vectorial]]></kwd>
<kwd lng="es"><![CDATA[SVR]]></kwd>
<kwd lng="es"><![CDATA[Red bayesiana]]></kwd>
<kwd lng="es"><![CDATA[Apilamiento]]></kwd>
<kwd lng="es"><![CDATA[Bagging]]></kwd>
<kwd lng="es"><![CDATA[AdaBoost.]]></kwd>
<kwd lng="en"><![CDATA[Outliers]]></kwd>
<kwd lng="en"><![CDATA[Ensemble methods]]></kwd>
<kwd lng="en"><![CDATA[ARIMA]]></kwd>
<kwd lng="en"><![CDATA[Support vector regression]]></kwd>
<kwd lng="en"><![CDATA[SVR]]></kwd>
<kwd lng="en"><![CDATA[Bayesian network]]></kwd>
<kwd lng="en"><![CDATA[Stacking]]></kwd>
<kwd lng="en"><![CDATA[Bagging]]></kwd>
<kwd lng="en"><![CDATA[AdaBoost]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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