<?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>2227-1899</journal-id>
<journal-title><![CDATA[Revista Cubana de Ciencias Informáticas]]></journal-title>
<abbrev-journal-title><![CDATA[Rev cuba cienc informat]]></abbrev-journal-title>
<issn>2227-1899</issn>
<publisher>
<publisher-name><![CDATA[Editorial Ediciones Futuro]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2227-18992019000300059</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Capacidad predictiva de las Máquinas de Soporte Vectorial. Una aplicación en la planificación financiera.]]></article-title>
<article-title xml:lang="en"><![CDATA[Predictive power of the Support Vector Machine. An application to the financial planning]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cuevas Soto]]></surname>
<given-names><![CDATA[Ing. Victor Manuel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Alvares Iriarte]]></surname>
<given-names><![CDATA[Lic. Susana]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Azcona Romero]]></surname>
<given-names><![CDATA[Lic. Mileydis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rodríguez Rogert]]></surname>
<given-names><![CDATA[Msc. A. Israel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Empresa de Mantenimiento a Centrales Eléctricas  ]]></institution>
<addr-line><![CDATA[ La Habana]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Empresa de Mantenimiento a Centrales Eléctricas  ]]></institution>
<addr-line><![CDATA[ La Habana]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Empresa de Mantenimiento a Centrales Eléctricas  ]]></institution>
<addr-line><![CDATA[ La Habana]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Oficina Central de la Unión Eléctrica  ]]></institution>
<addr-line><![CDATA[ La Habana]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2019</year>
</pub-date>
<volume>13</volume>
<numero>3</numero>
<fpage>59</fpage>
<lpage>75</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2227-18992019000300059&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2227-18992019000300059&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2227-18992019000300059&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN En los últimos años el uso de modelos no lineales para predicción y clasificación a través de algoritmos y técnicas computacionales de avanzada (Machine Learning), han surgido como alternativa eficiente para modelar procesos económicos. El presente trabajo fue desarrollado con el objetivo de construir un modelo para pronosticar la proyección de los ingresos en la Empresa de Mantenimiento a Centrales Eléctricas (EMCE). La modelación fue realizada a través del algoritmo Support Vector Machine (SVM) obteniéndose 95.8 % de aciertos con función kernel RBF. Se propone una herramienta gráfica (graph of prognostic projections) para la interpretación de pronóstico sobre proyecciones de variables de naturaleza económica.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT In recent years, the use of non-linear models for prediction and classification through algorithms and advanced computational techniques (Machine Learning) have emerged as an efficient alternative for modeling economic processes. The present work was developed with the objective of building a model to forecast the projection of revenues in the Maintenance Company to Power Plants (EMCE). The modeling was carried out through the Support Vector Machine (SVM) algorithm, obtaining 95.8% of hits with the RBF kernel function. A graphical tool (graph of prognostic projections) is proposed for the interpretation of forecasts about projections of variables of an economic nature.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[pronóstico]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[series de tiempo]]></kwd>
<kwd lng="es"><![CDATA[máquinas de soporte vectorial]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje automático.]]></kwd>
<kwd lng="en"><![CDATA[Forecasting]]></kwd>
<kwd lng="en"><![CDATA[Artificial Intelligence]]></kwd>
<kwd lng="en"><![CDATA[Time Series]]></kwd>
<kwd lng="en"><![CDATA[Support Vector Machine]]></kwd>
<kwd lng="en"><![CDATA[Machine Learning]]></kwd>
</kwd-group>
</article-meta>
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