<?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>S0379-39822020000200137</article-id>
<article-id pub-id-type="doi">10.18845/tm.v33i2.4073</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Discovery of Meaningful Rules by using DTW based on Cubic Spline Interpolation]]></article-title>
<article-title xml:lang="es"><![CDATA[Descubrimiento de reglas significativas mediante el uso de DTW basado en Interpolación Spline Cúbico]]></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[Alfaro-Barboza]]></surname>
<given-names><![CDATA[David-Eías]]></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[Cartago ]]></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>06</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2020</year>
</pub-date>
<volume>33</volume>
<numero>2</numero>
<fpage>137</fpage>
<lpage>149</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S0379-39822020000200137&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S0379-39822020000200137&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S0379-39822020000200137&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The ability to make short or long term predictions is at the heart of much of science. In the last decade, the data science community have been highly interested in foretelling real life events, using data mining techniques to discover meaningful rules or patterns, from different data types, including Time Series. Short-term predictions based on &#8220;the shape&#8221; of meaningful rules lead to a vast number of applications. The discovery of meaningful rules is achieved through efficient algorithms, equipped with a robust and accurate distance measure. Consequently, it is important to wisely choose a distance measure that can deal with noise, entropy and other technical constraints, to get accurate outcomes of similarity from the comparison between two time series. In this work, we do believe that Dynamic Time Warping based on Cubic Spline Interpolation (SIDTW), can be useful to carry out the similarity computation for two specific algorithms: 1- DiscoverRules() and 2- TestRules(). Mohammad Shokoohi-Yekta et al developed a framework, using these two algoritghms, to find and test meaningful rules from time series. Our research expanded the scope of their project, adding a set of well-known similarity search measures, including SIDTW as novel and enhanced version of DTW.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La capacidad de hacer predicciones a corto o largo plazo está en el corazón de gran parte de la ciencia. En la última década, la comunidad de ciencia de datos ha estado muy interesada en predecir eventos de la vida real, utilizando técnicas de minería de datos para descubrir reglas o patrones significativos, de diferentes tipos de datos, incluidas las series temporales. Las predicciones a corto plazo basadas en &#8220;la forma&#8221; de reglas significativas conducen a una gran cantidad de aplicaciones. El descubrimiento de reglas significativas se logra a través de algoritmos eficientes, equipados con una medida de distancia robusta y precisa. En consecuencia, es importante elegir sabiamente una medida de distancia que pueda lidiar con el ruido, la entropía y otras restricciones técnicas, para obtener resultados precisos de similitud a partir de la comparación entre dos series de tiempo. En este trabajo, creemos que Dynamic Time Warping (DTW) basada en la interpolación de splines cúbicos (SIDTW) puede ser útil para llevar a cabo el cálculo de similitud para dos algoritmos específicos: 1- DiscoverRules() y 2- TestRules(). Mohammad Shokoohi-Yekta et al. desarrollaron un marco, utilizando estos dos algoritmos, para encontrar y probar reglas significativas de series de tiempo. Nuestra investigación amplió el alcance de su proyecto, agregando un conjunto de medidas de búsqueda de similitud bien conocidas, incluyendo SIDTW como una versión novedosa y mejorada de DTW.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[DTW]]></kwd>
<kwd lng="es"><![CDATA[SIDTW]]></kwd>
<kwd lng="es"><![CDATA[Series de tiempo]]></kwd>
<kwd lng="es"><![CDATA[Descubrimiento de reglas]]></kwd>
<kwd lng="es"><![CDATA[Motif]]></kwd>
<kwd lng="en"><![CDATA[DTW]]></kwd>
<kwd lng="en"><![CDATA[SIDTW]]></kwd>
<kwd lng="en"><![CDATA[Time Series]]></kwd>
<kwd lng="en"><![CDATA[Rule Discovery]]></kwd>
<kwd lng="en"><![CDATA[Motif]]></kwd>
</kwd-group>
</article-meta>
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