{"id":130355,"date":"2021-06-07T13:56:52","date_gmt":"2021-06-07T17:56:52","guid":{"rendered":"https:\/\/www.sightline.com\/?p=130355"},"modified":"2026-02-17T15:48:10","modified_gmt":"2026-02-17T20:48:10","slug":"data-forecasting","status":"publish","type":"post","link":"https:\/\/www.sightline.com\/es\/data-forecasting\/blogs\/","title":{"rendered":"Previsi\u00f3n de datos"},"content":{"rendered":"<h2>Resumen del sector<\/h2><h2>Qu\u00e9 es la previsi\u00f3n de datos<\/h2><div class='yarpp yarpp-related yarpp-related-shortcode yarpp-template-list'>\n<!-- YARPP List -->\n<h3>Entradas relacionadas:<\/h3><ul>\n<li><a href=\"https:\/\/www.sightline.com\/es\/sightline-edm-security-event-detection\/use-cases\/\" rel=\"bookmark\" title=\"Detecci\u00f3n de eventos de seguridad de Sightline EDM: Fortalecimiento de los bancos europeos ante las estrictas pruebas de resistencia y normativas de la UE\">Detecci\u00f3n de eventos de seguridad de Sightline EDM: Fortalecimiento de los bancos europeos ante las estrictas pruebas de resistencia y normativas de la UE<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/es\/q-a-unleashing-productivity-in-manufacturing\/blogs\/\" rel=\"bookmark\" title=\"Anal\u00edtica avanzada en la fabricaci\u00f3n 101: Liberar la productividad y la rentabilidad\">Anal\u00edtica avanzada en la fabricaci\u00f3n 101: Liberar la productividad y la rentabilidad<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/es\/gnsol-partnership\/blogs\/\" rel=\"bookmark\" title=\"Sightline Systems se asocia con GNSOL para ofrecer soluciones inform\u00e1ticas en el extranjero\">Sightline Systems se asocia con GNSOL para ofrecer soluciones inform\u00e1ticas en el extranjero<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/es\/case-study-federal-defense-capacity-planning-2\/use-cases\/\" rel=\"bookmark\" title=\"Estudio de caso: Descubra c\u00f3mo Sightline mejor\u00f3 la planificaci\u00f3n de la capacidad de defensa federal mediante an\u00e1lisis avanzados y estrategias basadas en datos.\">Estudio de caso: Descubra c\u00f3mo Sightline mejor\u00f3 la planificaci\u00f3n de la capacidad de defensa federal mediante an\u00e1lisis avanzados y estrategias basadas en datos.<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/es\/data-analysis-iiot-systems-guide\/blogs\/\" rel=\"bookmark\" title=\"Aplicaci\u00f3n de herramientas de an\u00e1lisis de datos para sistemas IIoT\">Aplicaci\u00f3n de herramientas de an\u00e1lisis de datos para sistemas IIoT<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/es\/iiot\/blogs\/\" rel=\"bookmark\" title=\"IIoT: Internet industrial de los objetos\">IIoT: Internet industrial de los objetos<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/es\/what-is-mqtt\/blogs\/\" rel=\"bookmark\" title=\"Protocolo MQTT\">Protocolo MQTT<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/es\/zero-trust-data-security\/blogs\/\" rel=\"bookmark\" title=\"Seguridad de datos de confianza cero\">Seguridad de datos de confianza cero<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/es\/correlation-data-analysis-engine-brief\/blogs\/\" rel=\"bookmark\" title=\"Correlaci\u00f3n de datos\">Correlaci\u00f3n de datos<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/es\/case-study-global-travel-company-root-cause-analysis\/use-cases\/\" rel=\"bookmark\" title=\"Caso pr\u00e1ctico: Una empresa mundial de viajes utiliza el an\u00e1lisis de causa ra\u00edz y el an\u00e1lisis predictivo para 20.000 servidores\">Caso pr\u00e1ctico: Empresa de viajes global utiliza el an\u00e1lisis de causa ra\u00edz y el an\u00e1lisis predictivo para 20 000 servidores<\/a><\/li>\n<\/ul>\n<\/div>\n<h5>Sightline tiene experiencia en recopilar, conservar y analizar series temporales de datos para aplicar previsiones de datos.<\/h5>\n<p>&nbsp;<\/p>\n<h2>\u00bfQu\u00e9 son los datos de series temporales?<\/h2>\n<p>Los datos de series temporales son una colecci\u00f3n de cantidades que se re\u00fanen a lo largo de intervalos uniformes en el tiempo y se ordenan cronol\u00f3gicamente.  Las caracter\u00edsticas estad\u00edsticas de los datos de series temporales a menudo violan los supuestos de los m\u00e9todos estad\u00edsticos convencionales. Por ello, el an\u00e1lisis de datos de series temporales requiere un conjunto \u00fanico de herramientas y m\u00e9todos, conocidos colectivamente como an\u00e1lisis de series temporales.<\/p>\n<p><strong>La previsi\u00f3n es una de las formas de analizar series temporales para averiguar las tendencias futuras de los datos de entrada dados.<\/strong><\/p>\n<h2>Hay dos clases de t\u00e9cnicas populares de previsi\u00f3n de datos<\/h2>\n<p><strong>Series temporales univariantes:<\/strong> S\u00f3lo una variable var\u00eda con el tiempo. Por ejemplo, los datos recogidos de un sensor que mide la velocidad de un motor cada segundo. Por lo tanto, cada segundo, s\u00f3lo tendr\u00e1 un valor unidimensional, que es la velocidad.<\/p>\n<p><strong>Series temporales multivariantes:<\/strong> M\u00faltiples variables var\u00edan con el tiempo. Por ejemplo, un aceler\u00f3metro triaxial. Hay tres aceleraciones, una para cada eje (x,y,z) y var\u00edan simult\u00e1neamente con el tiempo.<\/p>\n<p>Existen m\u00faltiples m\u00e9todos o f\u00f3rmulas de previsi\u00f3n en cada t\u00e9cnica. GARCH, SARIMA, etc., son ejemplos de t\u00e9cnicas univariantes. VAR, VECM, etc., son ejemplos de t\u00e9cnicas multivariantes.<\/p>\n<h2>C\u00f3mo elegir el modelo adecuado de previsi\u00f3n de datos<\/h2>\n<p>Aparte de las t\u00e9cnicas univariantes frente a las multivariantes, la estructura (o) el comportamiento de los datos es lo que influye a la hora de elegir el \"modelo\" o la \"f\u00f3rmula de previsi\u00f3n\" adecuados. Algunos de los factores que influyen en el comportamiento de los datos son: la estacionalidad, la tendencia (lineal o no lineal), etc. de los datos.<\/p>\n<p>Por ejemplo, cuando se utilizan datos univariantes, SARIMA con los par\u00e1metros adecuados podr\u00eda ser un buen modelo para utilizar cuando se trata de datos de series temporales con estacionalidad. Si no existe estacionalidad en los datos, ARIMA podr\u00eda ser un buen enfoque.<\/p>\n<p><strong>SARIMA:<\/strong> Se refiere al ARIMA estacional. <em>series con componente de estacionalidad <\/em>en ellos que no ha sido manejado de ninguna manera. Aqu\u00ed tenemos que proporcionar <em>7 par\u00e1metros<\/em> donde 3 para ARIMA(AR, I, MA) y 3 para ARIMA estacional(Season AR, Season I, Season MA) y uno para la duraci\u00f3n de la estacionalidad(12 meses,6 meses seg\u00fan los datos)<\/p>\n<p><strong>ARIMA:<\/strong> Es<em> AutoRegresi\u00f3n Media m\u00f3vil integrada<\/em>. Aqu\u00ed T\u00e9rmino integrado se refiere a la diferenciaci\u00f3n, es decir, por ejemplo, calcular 5\u00ba t\u00e9rmino con orden 3, ser\u00eda 4\u00ba + (4\u00ba-3\u00ba)+(3\u00ba-2\u00ba) t\u00e9rminos.<\/p>\n<p>Las capacidades anal\u00edticas de Sightline EDM procesan la previsi\u00f3n de datos de series temporales para ayudar a los clientes a identificar las tendencias futuras de los datos de sus m\u00e1quinas utilizando las t\u00e9cnicas descritas anteriormente. La ventaja de Sightline EDM es que analiza autom\u00e1ticamente los datos para identificar su estacionalidad y aplica m\u00e9todos de previsi\u00f3n adecuados para identificar tendencias futuras.<\/p><h2>Contenido relacionado:<\/h2>","protected":false},"excerpt":{"rendered":"<p>La previsi\u00f3n utiliza datos hist\u00f3ricos para realizar estimaciones informadas que permiten predecir la direcci\u00f3n de las tendencias futuras. La elecci\u00f3n de las t\u00e9cnicas de previsi\u00f3n adecuadas depende del tipo de datos utilizados y de su comportamiento. Por ejemplo, las t\u00e9cnicas de previsi\u00f3n utilizadas para \"datos financieros\" pueden no ser apropiadas para series temporales de datos como \"datos de servidores inform\u00e1ticos\" o \"datos de actividad de redes de centros de datos\" (aunque los principios de previsi\u00f3n subyacentes siguen siendo los mismos).<\/p>","protected":false},"author":30,"featured_media":225027,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"footnotes":""},"categories":[4032],"tags":[261,3968],"class_list":["post-130355","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blogs","tag-forecasting","tag-industry-briefs"],"yoast_head":"<!-- This site is optimized with the 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