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6.49927C28.04 6.17205 28.0174 5.66432 27.6902 5.35966V5.34837C27.363 5.055 26.8552 5.07755 26.5506 5.40477L18.7311 13.9577C18.686 14.0141 18.6296 14.0592 18.5731 14.1156C17.2981 15.3117 15.3009 15.2327 14.1162 13.9577L6.2854 5.39351C5.98075 5.05501 5.47299 5.03242 5.14577 5.33708C4.80727 5.64173 4.7847 6.1495 5.08935 6.47672L10.8327 12.7503L5.6084 17.4217C5.2699 17.7151 5.24733 18.2228 5.5407 18.5613C5.83407 18.8998 6.35311 18.9224 6.68033 18.629L11.9159 13.9464L12.9201 15.0409C13.8454 16.0564 15.0865 16.6093 16.418 16.6093C17.7495 16.6093 18.9906 16.0564 19.9159 15.0409L20.8637 14.0028L26.0428 18.6516H26.0654C26.4039 18.945 26.9117 18.9111 27.205 18.5726C27.4984 18.2341 27.4645 17.7264 27.126 17.433L21.9695 12.818L27.7466 6.49927Z\" fill=\"#249EDC\"/\u003E\r\n\u003C/svg\u003E\u003C/span\u003E\r\n\u003C/div\u003E","cssContent":".share-icon__svg *{pointer-events:none}.share-icon-group{display:flex;gap:24px;align-items:center}.share-icon{display:inline-block}.share-icon svg{height:32px}.share-icon__svg:hover path{fill:var(--ui-02);transition:300ms ease fill}.share-icon__svg path{transition:300ms ease fill}","jsContent":"const pageURL = window.location.href;\r\n\r\ndocument.addEventListener('click', (e) =\u003E {\r\n\r\nconst targetClassesArr = [...e.target.classList];\r\nconsole.log(targetClassesArr);\r\nif (targetClassesArr.includes('share-icon--linkedin')) {\r\nconst shareURL = `https://www.linkedin.com/sharing/share-offsite/?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--facebook')) {\r\nconst shareURL = `http://www.facebook.com/sharer/sharer.php?u=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--twitter')) {\r\nconst shareURL = `https://twitter.com/intent/tweet?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--email')) {\r\nconst shareURL = `mailto:?subject=Check out this news from Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"}},":itemsOrder":["markup_editor"]}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/es/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"]},"flexible_column_content_container_2":{"id":"container-4abfafadf7","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-large",":items":{"container":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"overview","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-d41daf6c3f","type":"heading2","lines":["Descripción general"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-61ede876f2","text":"\u003Cp\u003EEl análisis de series temporales es una técnica que mide una variable a intervalos de tiempo regulares, lo que permite observar y analizar las características de esos datos. Se puede utilizar para hacer un seguimiento de métricas a lo largo del tiempo y analizar datos históricos en busca de tendencias, lo que la convierte en una herramienta indispensable para una amplia variedad de casos de uso. Se utiliza de forma generalizada en muchos sectores, especialmente en aquellos que recopilan grandes volúmenes de datos, como la \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/healthcare-and-life-sciences/\"\u003Esanidad\u003C/a\u003E, las \u003Ca href=\"https://www.snowflake.com/en/blog/streamlining-financial-market-intelligence-time-series-innovations/\"\u003Efinanzas\u003C/a\u003E, la \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/technology/\"\u003Etecnología\u003C/a\u003E y la logística. \u003C/p\u003E\r\n\u003Cp\u003EAl identificar tendencias históricas y correlaciones, los analistas pueden utilizar el \u003Ca href=\"https://www.snowflake.com/en/blog/time-series-analytics-asof-join-generally-available/\"\u003Eanálisis de series temporales\u003C/a\u003E para realizar predicciones fundamentadas sobre aspectos como la demanda de productos o las necesidades informáticas. El crecimiento de la \u003Ca href=\"https://www.snowflake.com/en/product/features/data-ingestion/\"\u003Erecopilación\u003C/a\u003E, el procesamiento y el almacenamiento de datos en casi todos los sectores de la economía ha incrementado el valor de esta técnica, al igual que la introducción del \u003Ca href=\"https://www.snowflake.com/es/product/ai/\"\u003Eaprendizaje automático (ML) y la inteligencia artificial (IA)\u003C/a\u003E. En este artículo exploramos en detalle el análisis de series temporales, por qué resulta útil y cómo se puede utilizar para mejorar los resultados empresariales.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"a","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-3ef1ad3b2a","additionalClasses":"headline-decoration","type":"heading2","lines":["¿Qué es el análisis de series temporales?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-742c85a028","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEl análisis de series temporales puede parecer engañosamente simple, lo que oculta su flexibilidad y su potencial complejidad. Todos los datos de series temporales se presentan como una colección ordenada de datos recopilados a lo largo del tiempo en intervalos uniformes. Un ejemplo sería el número de suscripciones a un boletín informativo que recibe una empresa cada mes durante un año. Puede resultar especialmente eficaz cuando se aplica a datos históricos, ya que ampliar la escala temporal de la serie permite observar tendencias a largo plazo y, por ejemplo, identificar el impacto de cambios en el entorno económico, modificaciones en las políticas públicas o interrupciones en la cadena de suministro. \u003C/p\u003E\n\u003Cp\u003ELa característica que define esta técnica, como cabría esperar, es el ritmo constante del paso del tiempo, en contraste con técnicas como el análisis de regresión, que examina la relación entre variables, o el análisis transversal, que analiza distintos tipos de datos en un único momento.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy_copy_":{"columnClassNames":{"container_copy_copy__987904085":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__643019817":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__749217882":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1985496925":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__127842237":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1720648998":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_259718203":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1190438074":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__493640578":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"rel","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"container_copy_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"is","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-b13f9c4235","additionalClasses":"headline-decoration","type":"heading2","lines":["Componentes de los datos de series temporales"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-d4447e6b5f","text":"\u003Cp\u003ELos datos utilizados para crear una serie temporal pueden presentar determinadas características, que pueden desglosarse en los siguientes elementos:\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ETendencia\u003C/h4\u003E\n\u003Cp\u003ELa tendencia describe la dirección en la que evoluciona una métrica, si existe. Siguiendo con el ejemplo anterior, podría descubrirse que las suscripciones al boletín crecen mes a mes, lo que indicaría que la tendencia es ascendente.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EEstacionalidad\u003C/h4\u003E\n\u003Cp\u003ELa estacionalidad describe un patrón recurrente en los datos que se repite en un ciclo, normalmente vinculado a un evento predecible y constante. Uno de los ejemplos más habituales es el aumento del gasto de los consumidores durante las vacaciones, aunque un ejemplo más específico podría ser el incremento repentino de apartamentos disponibles para alquilar los días 1 y 15 de cada mes.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EPatrones cíclicos\u003C/h4\u003E\n\u003Cp\u003ELos ciclos describen patrones a largo plazo que no están definidos por una estación o un evento concreto. Suelen hacer referencia a la expansión y contracción económica, que tiende a producirse a lo largo de periodos prolongados y no está ligada a un único acontecimiento. Algunos ejemplos son la reducción del gasto en software empresarial durante una recesión y el comportamiento inverso durante un periodo de crecimiento económico.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ERuido irregular o aleatorio\u003C/h4\u003E\n\u003Cp\u003EEl ruido describe puntos de datos que no pueden explicarse mediante ninguno de los otros elementos, como un problema técnico que provoca una desviación respecto a la norma, un hecho puntual u otro evento no definido. Los errores de sensores en maquinaria o las pequeñas fluctuaciones en los precios de las acciones de un minuto a otro son buenos ejemplos de ruido.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy_":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"use","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-3ef36f89c3","additionalClasses":"headline-decoration","type":"heading2","lines":["Tipos de datos de series temporales"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-ff7fa68da8","text":"\u003Cp\u003ELos datos de series temporales pueden clasificarse en distintos tipos en función del número de variables, la forma en que se recopilan los datos u otra información estadística sobre la serie temporal. A continuación se muestran tres métodos principales de categorización de los datos de series temporales.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ESeries temporales univariantes o multivariantes\u003C/h4\u003E\n\u003Cp\u003EUna serie temporal univariante realiza el seguimiento de una única métrica en intervalos definidos. Un ejemplo habitual son los latidos por minuto (BPM), la medida estándar de la frecuencia cardíaca de una persona. Una serie temporal multivariante utiliza el mismo intervalo, pero realiza el seguimiento de varias métricas a lo largo del tiempo. Por ejemplo, un hospital puede registrar y supervisar al mismo tiempo la frecuencia cardíaca y el nivel de oxígeno en sangre de un paciente, ya que la combinación de ambos ofrece una visión más completa de la salud circulatoria. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EIntervalos de tiempo regulares o irregulares\u003C/h4\u003E\n\u003Cp\u003ELos intervalos regulares registran valores con una cadencia temporal constante; por ejemplo, la temperatura de una habitación medida una vez por minuto. Un intervalo de tiempo irregular registra eventos discretos a medida que se producen, como el seguimiento de cada solicitud procesada por un servicio en la nube, que puede concentrarse varias veces en un breve periodo o no producirse en absoluto. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ESeries temporales estacionarias o no estacionarias\u003C/h4\u003E\n\u003Cp\u003ELos modelos de previsión de series temporales se basan en datos estacionarios, en los que el rango de valores es predecible a lo largo del tiempo y no varía en sus propiedades estadísticas, como la media y la varianza. Los datos no estacionarios cambian, a menudo debido a la estacionalidad u otro tipo de volatilidad. Las series estacionarias son más fiables y fáciles de analizar que las no estacionarias, aunque técnicas estadísticas como la diferenciación pueden hacer que los datos no estacionarios sean lo suficientemente estables para la previsión.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy_259718203":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"and","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-4daeb24959","additionalClasses":"headline-decoration","type":"heading2","lines":["Por qué utilizar el análisis de datos de series temporales"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-c8c9583607","text":"\u003Cp\u003ELas series temporales pueden utilizarse en una amplia variedad de contextos, algunos centrados en la mitigación de riesgos y la disponibilidad del servicio, y otros orientados al análisis en profundidad de datos históricos para descubrir patrones útiles.\u003C/p\u003E\n\u003Cp\u003EAlgunos de los casos de uso más sencillos se centran en establecer una referencia para una actividad, que puede ir desde la latencia de los servicios informáticos hasta el número de interacciones que generan las acciones de marketing cada día. Una serie temporal permite supervisar estos escenarios para detectar comportamientos anómalos. Por ejemplo, un pico anómalo de latencia puede indicar la necesidad de realizar tareas de mantenimiento de red, mientras que un aumento repentino de las interacciones de marketing puede aportar información valiosa sobre qué mensajes están funcionando mejor.\u003C/p\u003E\n\u003Cp\u003ETambién se pueden utilizar series temporales para tomar decisiones fundamentadas. Un ejemplo habitual es la gestión del inventario. Al analizar los aumentos y descensos históricos de la demanda de los clientes, se pueden optimizar la cadena de suministro y el calendario de producción para garantizar que los productos y servicios se amplíen para satisfacer la demanda y se ajusten para maximizar el ahorro de costes.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__987904085":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"how","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-4638bb2d77","additionalClasses":"headline-decoration","type":"heading2","lines":["Métodos y técnicas de análisis de series temporales"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-ac34111e69","text":"\u003Cp\u003EExisten diversos métodos para extraer información útil a partir del análisis de series temporales. El método elegido dependerá de las características de la serie temporal y de la disponibilidad de datos históricos. Estos son algunos de los métodos de análisis de series temporales más utilizados.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EMedias móviles y suavizado exponencial\u003C/h4\u003E\r\n\u003Cp\u003EAmbos métodos utilizan medias de datos históricos para reducir el impacto de las fluctuaciones en una métrica. Al eliminar el ruido y otros cambios anómalos, una media móvil puede ayudar a identificar una tendencia general en los datos a lo largo del tiempo. La media móvil del precio de una acción refleja la trayectoria general de ese precio durante un periodo determinado, en lugar de cambios impredecibles al alza o a la baja. El suavizado exponencial es una técnica que da mayor peso a los datos más recientes que a los más antiguos. Esto proporciona una media que incorpora los cambios recientes y permite prever una tendencia que refleje el estado actual de los datos. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EModelos ARIMA y SARIMA\u003C/h4\u003E\r\n\u003Cp\u003EEl modelo autorregresivo integrado de media móvil (ARIMA) combina tres técnicas (autorregresión, diferenciación y media móvil) para prever variables futuras. Al combinar estas técnicas, ARIMA puede utilizar una serie temporal no estacionaria para ofrecer predicciones precisas, ya que estima cómo cambiará la variable en función de la media móvil de los datos históricos. SARIMA (modelo autorregresivo integrado de media móvil estacional) tiene en cuenta los cambios estacionales en los datos, lo que permite realizar previsiones a partir de datos con estacionalidad.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EAprendizaje automático para la previsión de series temporales\u003C/h4\u003E\r\n\u003Cp\u003EMuchas organizaciones incorporan técnicas de ML, como redes neuronales recurrentes o modelos de árboles de decisión, en sus procesos de análisis, ya que estos modelos pueden generar previsiones más precisas y procesar volúmenes de datos mucho mayores. Además, pueden automatizar los aspectos del análisis que requieren más tiempo y esfuerzo, lo que aumenta la eficiencia de la organización.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text_copy_1241045613"]},"container_copy_copy__1985496925":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"why","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-35e7dcd0e7","additionalClasses":"headline-decoration","type":"heading2","lines":["Lista de comprobación para el análisis de series temporales"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-3720273b6c","text":"\u003Cp\u003EIncluso con potentes herramientas de aprendizaje automático, implementar con éxito un análisis de series temporales exige tener muy en cuenta los objetivos y las limitaciones del proyecto. A continuación se describen los cinco pasos que deben seguirse al iniciar un nuevo proyecto de análisis de series temporales.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E1. Definir el objetivo\u003C/h4\u003E\r\n\u003Cp\u003ETener una idea clara de lo que se espera obtener es importante no solo para evaluar el éxito, sino también para comprender las limitaciones del conjunto de datos.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E2. Preparar y limpiar los datos\u003C/h4\u003E\r\n\u003Cp\u003EUna vez que el objetivo esté claro, hay que recopilar un conjunto de datos y utilizar técnicas de suavizado para limpiarlo, abordando los valores atípicos, los valores ausentes y cualquier otro problema del conjunto de datos.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E3. Visualizar y explorar patrones\u003C/h4\u003E\r\n\u003Cp\u003ELa visualización de los datos en forma gráfica ayuda a detectar patrones comunes, como la estacionalidad o las tendencias generales. El uso de una función de autocorrelación (ACF) puede ayudar a determinar si el conjunto de datos es estacionario o no.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E4. Elegir el método adecuado\u003C/h4\u003E\r\n\u003Cp\u003EEn función de los datos y del objetivo, se puede optar por aplicar ARIMA o SARIMA a conjuntos de datos en los que la estacionalidad sea relevante. Para conjuntos de datos más grandes o análisis más complejos, puede ser necesario utilizar técnicas de ML o aprendizaje profundo para obtener resultados.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E5. Validar e interpretar los resultados\u003C/h4\u003E\r\n\u003Cp\u003EExisten numerosas técnicas para validar si los resultados son precisos, normalmente mediante la comparación de la previsión con los resultados reales a medida que se producen. Una vez que se ha validado que el modelo predice con precisión los resultados futuros, puede utilizarse la previsión para tomar decisiones relacionadas con los objetivos definidos en el paso 1. Por ejemplo, predecir cómo podría cambiar la demanda de los consumidores en los próximos seis meses.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__127842237":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"customer","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-27de896328","additionalClasses":"headline-decoration","type":"heading2","lines":["Ejemplos y casos de uso del análisis de series temporales"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-2228af7077","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ETodos los sectores de la economía moderna utilizan algún tipo de análisis de datos para fundamentar la toma de decisiones. Estos son algunos de los casos de uso más habituales del análisis de series temporales en distintos sectores:\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ERetail y comercio electrónico\u003C/h4\u003E\n\u003Cp\u003EEl análisis de series temporales se utiliza ampliamente en contextos de \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/retail-consumer-goods/\"\u003Eretail\u003C/a\u003E, tanto para hacer un seguimiento del interés de los clientes y la actividad de compra como para garantizar la disponibilidad del servicio. En el ámbito de la previsión, muchas organizaciones incorporan el análisis de series temporales en sus decisiones de gestión de la cadena de suministro, utilizando datos históricos para anticiparse a posibles interrupciones en el suministro y planificar los niveles de inventario de futuras promociones de ventas.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ESanidad y ciencias de la vida\u003C/h4\u003E\n\u003Cp\u003ELas organizaciones de \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/healthcare-and-life-sciences/\"\u003Easistencia sanitaria\u003C/a\u003E y bienestar utilizan ampliamente el análisis de series temporales, ya que puede aplicarse a múltiples ámbitos, desde electrocardiógrafos hasta \u003Ca href=\"https://www.snowflake.com/en/customers/all-customers/case-study/whoop/\"\u003Etecnología ponible\u003C/a\u003E. También se utiliza en la investigación científica, donde los investigadores pueden emplearlo para analizar grandes conjuntos de datos de una región o población concreta, como el análisis de aguas residuales para detectar aumentos de determinados virus.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EAnalíticas de marketing y de clientes\u003C/h4\u003E\n\u003Cp\u003EEl análisis de series temporales puede ser una herramienta clave para realizar el seguimiento del rendimiento de distintos tipos de campañas de \u003Ca href=\"https://www.snowflake.com/en/solutions/departments/marketing/\"\u003Emarketing\u003C/a\u003E. Esto permite, por ejemplo, establecer una referencia del número de nuevos clientes que se adquieren cada semana, que puede utilizarse como base para medir el impacto de nuevos mensajes, anuncios u otras iniciativas de marketing.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text_copy_1241045613"]},"container_copy_copy__1720648998":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"ai","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-4bef6f9a21","additionalClasses":"headline-decoration","type":"heading2","lines":["Ventajas del análisis de series temporales para el éxito empresarial"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-d4d77d5dc3","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEl análisis de series temporales es muy utilizado en muchos sectores porque permite a las organizaciones comprender mejor el pasado, planificar el futuro, adaptarse a las condiciones cambiantes del mercado y mejorar el rendimiento de los productos, todo ello a partir de datos históricos. A continuación se detallan algunos de los principales beneficios:\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E1. Mejora de la precisión de las previsiones\u003C/h4\u003E\r\n\u003Cp\u003ELa estructura del método de series temporales exige una limpieza exhaustiva de los datos y comprobaciones de estabilidad, lo que contribuye a que sea un modelo especialmente útil para \u003Ca href=\"https://www.snowflake.com/es/customers/all-customers/case-study/igs-energy/\"\u003Elas previsiones\u003C/a\u003E. Al tener en cuenta el ruido, las variables ausentes y otros posibles valores atípicos, y al contrastar las predicciones con observaciones fuera de la muestra, es posible mejorar las capacidades de previsión.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E2. Mejor planificación de recursos\u003C/h4\u003E\r\n\u003Cp\u003EAl proporcionar previsiones precisas sobre cambios en la demanda de los consumidores, los costes y las ineficiencias de la cadena de suministro o las exigencias computacionales de los sistemas informáticos, el análisis de series temporales ayuda a minimizar los gastos innecesarios sin afectar al rendimiento del producto ni a la disponibilidad del servicio.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E3. Supervisión del rendimiento en tiempo real\u003C/h4\u003E\r\n\u003Cp\u003EAdemás de su potente capacidad de previsión, el análisis de series temporales puede utilizarse en aplicaciones en tiempo real, lo que permite detectar rápidamente problemas como picos de latencia, aumentos repentinos de la demanda u otros cambios.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text_copy_1241045613"]},"container_copy_copy__643019817":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"of","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-d62beca6b0","additionalClasses":"headline-decoration","type":"heading2","lines":["Desafíos del análisis de series temporales"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-3cff6e868e","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEl análisis de series temporales es solo una herramienta dentro del conjunto de técnicas de análisis de datos y, por tanto, puede no ser el método más adecuado para todos los casos de uso. Además, su utilidad depende de disponer de datos de calidad que puedan procesarse de forma fiable, lo que no siempre es posible. Estos son algunos de los desafíos más habituales de esta disciplina.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ECalidad de los datos y valores ausentes\u003C/h4\u003E\n\u003Cp\u003EPor muy bien planteado que esté el enfoque, si los datos son incoherentes o presentan un gran número de valores ausentes, la precisión y la utilidad del modelo de series temporales se verán notablemente reducidas.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EPatrones complejos y no estacionarios\u003C/h4\u003E\n\u003Cp\u003EAunque es posible convertir conjuntos de datos en estacionarios mediante técnicas como la diferenciación, esto puede generar nuevos problemas, como el ruido. Los modelos de análisis de series temporales pueden tener dificultades con datos complejos; por ejemplo, métricas con múltiples patrones estacionales que varían según la hora, el día de la semana y el mes. Debido a esta complejidad, puede resultar más difícil obtener datos estacionarios y producir previsiones precisas.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ELimitaciones de escalabilidad y de procesamiento en tiempo real\u003C/h4\u003E\n\u003Cp\u003EDado que la previsión de series temporales depende de datos históricos, puede resultar complejo ajustar, probar y ejecutar modelos de series temporales con grandes volúmenes de datos. Esto puede provocar que las operaciones alcancen sus límites arquitectónicos o que su ejecución resulte costosa a medida que crece el conjunto de datos.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text_copy_1241045613"]},"container_copy_copy__749217882":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"con","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-16de2d4fd8","additionalClasses":"headline-decoration","type":"heading2","lines":["Conclusión"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-7086c5d1d3","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEl análisis de series temporales es una de las técnicas analíticas más valiosas para las empresas, ya que permite tomar decisiones fundamentadas basadas en previsiones precisas e identificar oportunidades de optimización de la eficiencia estructural y otras mejoras. El crecimiento y la potencia de las herramientas de ML e IA han hecho que este método sea más eficiente y accesible, permitiendo incluso a equipos no técnicos crear modelos para medir el rendimiento y mejorar las previsiones. No es de extrañar que el análisis de series temporales siga siendo uno de los principales métodos analíticos utilizados por las \u003Ca href=\"https://www.snowflake.com/es/product/analytics/\"\u003Eplataformas de analíticas modernas\u003C/a\u003E.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text_copy_1241045613"]},"container_copy_copy__493640578":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","simple_snowflake_acc":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"time","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-f4bee42eaf","additionalClasses":"headline-decoration","type":"heading2","lines":["Preguntas frecuentes sobre el análisis de series temporales"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-be5d7e2cd0","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEstas son algunas de las preguntas más habituales sobre el análisis de series temporales.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-98c27e89af","showDivider":false,"accordionItemsList":[{"title":"¿Cuál es la diferencia entre el análisis de series temporales y la previsión de series temporales?","richText":"\u003Cp\u003EEl análisis de series temporales consiste en analizar datos actuales e históricos para extraer nueva información; por ejemplo, medir el rendimiento de una campaña de marketing en comparación con campañas anteriores a partir de métricas como tasas de clics, clientes potenciales cualificados u otras. La previsión de series temporales plantea un modelo que utiliza datos históricos para prever valores futuros de la serie; por ejemplo, proporciona una \u003Ca href=\"https://www.snowflake.com/en/fundamentals/predictive-analytics-marketing/\"\u003Epredicción del impacto que podría tener una nueva campaña de marketing\u003C/a\u003E en función de su rendimiento pasado. La previsión de series temporales se considera un subconjunto del análisis de series temporales.\u003C/p\u003E\n"},{"title":"¿Qué es un modelo de previsión de series temporales?","richText":"\u003Cp\u003EUn modelo de previsión es un método que se utiliza para realizar predicciones fundamentadas y precisas de los valores futuros en un conjunto de datos de series temporales. Elegir el modelo adecuado es un componente clave para el éxito de la previsión de series temporales. Los analistas tienen en cuenta factores como la estacionariedad del conjunto de datos, su complejidad y los objetivos generales para seleccionar el modelo más adecuado.\u003C/p\u003E\n"},{"title":"¿Cuáles son las limitaciones del análisis de series temporales?","richText":"\u003Cp\u003EAunque es una herramienta potente, el análisis de series temporales puede no ser el método ideal en todas las situaciones. Muchos métodos de series temporales requieren grandes volúmenes de datos históricos para ofrecer análisis precisos, y modelos como ARIMA y SARIMA suelen necesitar un ajuste considerable para generar predicciones fiables. Muchos modelos también presentan dificultades ante la complejidad, como la presencia de múltiples tendencias estacionales. Algunos de estos problemas pueden abordarse con herramientas de ML o IA, pero el volumen de preprocesamiento de datos puede dar lugar a sobreajuste u otros errores. \u003C/p\u003E\n"}],":type":"snowflake-site/components/simple-snowflake-accordion","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text_copy_1241045613","simple_snowflake_acc"]},"container_copy_copy__1190438074":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"using","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-49d738aa96","additionalClasses":"headline-decoration","type":"heading2","lines":["Clientes que usan Snowflake para Analíticas "],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"flexible_column_cont":{"id":"flexible-column-container-73c51193bf","type":"2-column-even","alignColumns":"match-height","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-d809f1a44f","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"card_v2":{"id":"card-v2-07b1bb8d4e","configurationStatus":{"configured":true,"message":""},"text":{"id":"text","text":"\u003Cp\u003EGracias a Snowflake y Apache Iceberg, los equipos de WHOOP han centralizado el acceso a los datos, lo que a su vez ha reducido la complejidad y los costes, y ha mejorado los procesos esenciales, como el desarrollo de funciones y las previsiones financieras.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical","title":{"id":"title","type":"heading4","lines":["WHOOP mejora las previsiones financieras y la experiencia de los miembros con IA y ML"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/whoop/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Leer la historia"},"image":{"id":"image","lazyEnabled":true,"alt":"power digital logo","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--e77c4e06-e383-4fd9-9235-72a16163e0b4/whoop-customer-card%25402x.jpg?preferwebp=true&quality=85","height":"720","width":"1680",":type":"snowflake-site/components/image"},"type":"content-card",":type":"snowflake-site/components/card-v2"}},":itemsOrder":["card_v2"]},"flexible_column_content_container_2":{"id":"container-e434595c47","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"card_v2_copy":{"id":"card-v2-b62a51dd33","configurationStatus":{"configured":true,"message":""},"text":{"id":"text","text":"\u003Cp\u003EGracias a Snowflake, IGS Energy emplea los datos en distintos casos de uso de IA y aprendizaje automático (ML) —desde crear modelos de previsión más rentables hasta mejorar la precisión de la detección de anomalías— para hacer realidad su misión de 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