<?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>2007-7467</journal-id>
<journal-title><![CDATA[RIDE. Revista Iberoamericana para la Investigación y el Desarrollo Educativo]]></journal-title>
<abbrev-journal-title><![CDATA[RIDE. Rev. Iberoam. Investig. Desarro. Educ]]></abbrev-journal-title>
<issn>2007-7467</issn>
<publisher>
<publisher-name><![CDATA[Centro de Estudios e Investigaciones para el Desarrollo Docente A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-74672022000100048</article-id>
<article-id pub-id-type="doi">10.23913/ride.v12i24.1203</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Análisis comparativo de modelos tradicionales y modernos para pronóstico de la demanda: enfoques y características]]></article-title>
<article-title xml:lang="en"><![CDATA[Comparative Analysis of Traditional and Modern Models for Forecasting Demand: Approaches and Features]]></article-title>
<article-title xml:lang="pt"><![CDATA[Análise comparativa de modelos tradicionais e modernos de previsão de demanda: abordagens e características]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Fierro Torres]]></surname>
<given-names><![CDATA[César Ángel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Castillo Pérez]]></surname>
<given-names><![CDATA[Velia Herminia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Torres Saucedo]]></surname>
<given-names><![CDATA[Claudia Irene]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Tecnológico Nacional de México Instituto Tecnológico de Ciudad Juárez ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Tecnológico Nacional de México Instituto Tecnológico de Ciudad Juárez ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Centro de Bachillerato Tecnológico Industrial y de Servicios 128  ]]></institution>
<addr-line><![CDATA[Chihuahua ]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<volume>12</volume>
<numero>24</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-74672022000100048&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-74672022000100048&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-74672022000100048&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen En estadística inferencial, el pronóstico es un proceso matemático mediante el cual se hace una estimación del valor futuro de una o más variables, como puede ser la demanda. El objetivo de este presente trabajo de investigación documental fue definir la clasificación de los principales tipos de pronósticos. Además, proponer algunos de los modelos más representativos utilizados actualmente para ser implementados por pequeñas y medianas empresas, aquellos que, con base en la literatura consultada, tienen mayor potencial para lograr un pronóstico de demanda exitoso. Se encontró que los pronósticos pueden ser univariados o multivariados; sin embargo, a fin de encontrar las alternativas que impliquen menor costo para la empresa por procesamiento de datos, se consideraron exclusivamente modelos de pronósticos de series de tiempo univariados, ya que requieren únicamente los datos históricos de las ventas de la empresa. Los modelos de pronósticos de serie de tiempo se clasificaron en tres enfoques: 1) estadísticos o tradicionales, de los cuales se recomendaron el modelo de suavización exponencial triple o de Holt-Winters y el modelo de promedios móviles autorregresivos integrados (Arima), 2) de aprendizaje automático, de los cuales se destacaron el modelo bosque aleatorio y el modelo de redes neuronales recurrentes de gran memoria a corto plazo (LSTM), 3) híbridos, de los cuales se sugirieron el modelo Arima-LSTM y el modelo Facebook Prophet.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract In inferential statistics, forecasting is a mathematical process by which the future value of one or more variables, such as demand, is estimated. The objective of this paper was to define the classification of the main types of forecasts. In addition, to propose some of the most representative models currently used to be implemented by small and medium-sized companies, those that, based on the literature consulted, have the greatest potential to achieve a successful demand forecast. It was found that forecasts could be univariate or multivariate; however, in order to find the alternatives that would require lower cost for the enterprise by data processing, only univariate time series forecast models were recommended, as they require only historical sales data of the company. Time series forecasting models were classified into three approaches: 1) statistical or traditional, of which the Holt-Winters or triple exponential smoothing model and the autoregressive integrated moving average (Arima) model were recommended, 2) machine learning, of which the random forest model and the large short-term memory (LSTM) recurrent neural network model stood out, 3) hybrids, of which the Arima-LSTM model and the Facebook Prophet model were suggested.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo Na estatística inferencial, a previsão é um processo matemático pelo qual é feita uma estimativa do valor futuro de uma ou mais variáveis, como a demanda. O objetivo do presente trabalho de pesquisa documental foi definir a classificação dos principais tipos de previsões. Além disso, propor alguns dos modelos mais representativos atualmente utilizados para serem implementados por pequenas e médias empresas, aqueles que, com base na literatura consultada, possuem maior potencial para alcançar uma previsão de demanda bem-sucedida. Constatou-se que as previsões podem ser univariadas ou multivariadas; no entanto, para encontrar as alternativas que implicam no menor custo para a empresa para o processamento dos dados, foram considerados apenas modelos de previsão de séries temporais univariadas, uma vez que requerem apenas os dados históricos de vendas da empresa. Os modelos de previsão de séries temporais foram classificados em três abordagens: 1) estatística ou tradicional, sendo recomendado o modelo de Holt-Winters ou suavização exponencial tripla e o modelo integrado de médias móveis autorregressivas (Arima), 2) aprendizado de máquina, dos quais a floresta aleatória e o modelo de rede neural recorrente de grande memória de curto prazo (LSTM), 3) híbridos, dos quais foram sugeridos o modelo Arima-LSTM e o modelo Facebook Prophet.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[inferencia estadística]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[previsión]]></kwd>
<kwd lng="es"><![CDATA[series temporales]]></kwd>
<kwd lng="en"><![CDATA[statistical inference]]></kwd>
<kwd lng="en"><![CDATA[artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[prediction]]></kwd>
<kwd lng="en"><![CDATA[time series]]></kwd>
<kwd lng="pt"><![CDATA[inferência estatística]]></kwd>
<kwd lng="pt"><![CDATA[inteligência artificial]]></kwd>
<kwd lng="pt"><![CDATA[previsão]]></kwd>
<kwd lng="pt"><![CDATA[séries temporais]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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