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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"],":type":"snowflake-site/components/container"}},":itemsOrder":["container_949147658"],":type":"snowflake-site/components/container"},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/fr/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],":type":"snowflake-site/components/experiencefragment","classNames":"aem-xf","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-b385791062","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","layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-9eaa8fe437","type":"heading2","lines":["Présentation"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-7ce52596da","text":"\u003Cp\u003EL’analyse de séries temporelles est une technique qui mesure une variable à intervalles réguliers, afin de vous permettre d’observer et d’analyser les caractéristiques de ces données. Vous pouvez l’utiliser pour suivre des indicateurs au fil du temps et analyser des données historiques afin d’y repérer des tendances. Ainsi, c’est un outil indispensable pour un certain nombre de cas d’usage différents. De ce fait, l’analyse de séries temporelles est largement utilisée dans de nombreux secteurs, en particulier ceux qui collectent de grandes quantités de données, tels que les secteurs de la \u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/healthcare-and-life-sciences/\"\u003Esanté\u003C/a\u003E, des \u003Ca href=\"https://www.snowflake.com/fr/blog/streamlining-financial-market-intelligence-time-series-innovations/\"\u003Eservices financiers\u003C/a\u003E, de la \u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/technology/\"\u003Etechnologie\u003C/a\u003E et de la logistique. \u003C/p\u003E\n\u003Cp\u003EEn identifiant des tendances historiques et des corrélations, les analystes peuvent utiliser \u003Ca href=\"https://www.snowflake.com/en/blog/time-series-analytics-asof-join-generally-available/\"\u003El’analyse de séries temporelles\u003C/a\u003E pour formuler des prédictions éclairées sur des éléments tels que la demande de produits ou encore les besoins en calcul. La croissance de la \u003Ca href=\"https://www.snowflake.com/fr/product/features/data-ingestion/\"\u003Ecollecte\u003C/a\u003E, du traitement et du stockage de données dans tous les secteurs de l’économie ou presque a renforcé le potentiel de cette technique, tout comme l’introduction du \u003Ca href=\"https://www.snowflake.com/fr/product/ai/\"\u003Emachine learning et de l’IA\u003C/a\u003E. Dans cet article, nous allons étudier en détails l’analyse de séries temporelles, son intérêt et comment l’utiliser pour obtenir de meilleurs résultats stratégiques. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container"},"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","layout":"RESPONSIVE_GRID","columnCount":12,"id":"a","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-876f9da59d","additionalClasses":"headline-decoration","type":"heading2","lines":["En quoi consiste l’analyse de séries temporelles ?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-89862be79d","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EOn pourrait penser que l’analyse de séries temporelles est une technique simple, mais ce serait nier sa flexibilité et sa complexité potentielle. Toutes les données de séries temporelles sont présentées sous la forme d’un ensemble organisé de données capturées à intervalles réguliers au fil du temps. Il peut s’agir, par exemple, du nombre mensuel d’inscriptions à la newsletter d’une entreprise sur une période d’un an. L’analyse de séries temporelles peut être particulièrement intéressante lorsqu’elle est appliquée à des données historiques, car cela étend l’échelle de temps de la série et permet d’observer des tendances à long terme. Il devient ainsi possible, par exemple, de repérer l’impact des changements économiques, de l’évolution des politiques publiques ou des perturbations de la supply chain. \u003C/p\u003E\n\u003Cp\u003ESans surprise, la caractéristique déterminante de cette technique est le tic-tac imperturbable du temps, en contraste avec d’autres techniques comme l’analyse de régression qui examine la relation entre les variables, ou l’analyse cross-section qui examine divers types de données à un moment précis.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container"},"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","layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel","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","layout":"RESPONSIVE_GRID","columnCount":12,"id":"is","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-bcbc344508","additionalClasses":"headline-decoration","type":"heading2","lines":["Composants des données de séries temporelles"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-16ec1d549f","text":"\u003Cp\u003ELes données que vous utilisez pour créer une série temporelle peuvent présenter certaines caractéristiques, qui peuvent être divisées ainsi :\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ETendance\u003C/h4\u003E\n\u003Cp\u003ELa tendance décrit la direction que prend l’indicateur, le cas échéant. Pour reprendre notre exemple précédent, une entreprise peut constater une augmentation mois après mois des inscriptions à sa newsletter, c’est-à-dire une tendance à la hausse des inscriptions.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ESaisonnalité\u003C/h4\u003E\n\u003Cp\u003ELa saisonnalité décrit un schéma récurrent dans les données qui se produit sur un cycle défini, généralement lié à un événement prévisible et constant. L’un des exemples les plus courants est l’augmentation des dépenses des consommateurs pendant la période des fêtes de fin d’année ; autre exemple plus granulaire, l’augmentation soudaine des appartements disponibles à la location le 1\u003Csup\u003Eer\u003C/sup\u003E et le 15\u003Csup\u003Ee \u003C/sup\u003Ejour de chaque mois.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ESchémas cycliques\u003C/h4\u003E\n\u003Cp\u003ELes cycles décrivent des schémas sur le long terme qui ne sont pas liés à une saison ou un événement en particulier. On parle souvent de cycles concernant l’expansion et la contraction économiques, qui ont tendance à se produire sur de longues périodes sans être liées à un événement particulier. Elles peuvent par exemple prendre la forme d’une réduction des dépenses logicielles en entreprise pendant une récession et inversement pendant une période de croissance économique.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EBruit irrégulier ou aléatoire\u003C/h4\u003E\n\u003Cp\u003ELe bruit décrit des points de données qui n’ont aucune explication parmi les autres éléments de données, comme un problème technique qui entraîne un écart par rapport à la norme, des circonstances exceptionnelles ou tout autre événement non défini. Des erreurs de capteurs dans les machines ou des petites fluctuations des cours de la bourse d’une minute à l’autre illustrent bien cette notion de bruit.\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"],":type":"snowflake-site/components/container"},"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","layout":"RESPONSIVE_GRID","columnCount":12,"id":"use","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-9fd61aa04f","additionalClasses":"headline-decoration","type":"heading2","lines":["Types de données de séries temporelles"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-24cb5868ff","text":"\u003Cp\u003ELes données de séries temporelles peuvent être classées en différents types, en fonction du nombre de variables, de la façon dont les données sont recueillies ou d’autres informations statistiques sur les séries temporelles. Voici trois grandes méthodes de catégorisation des données de séries temporelles.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ESéries temporelles univariées et multivariées\u003C/h4\u003E\n\u003Cp\u003EUne série temporelle univariée suit un indicateur unique à intervalles définis. On en retrouve un exemple courant avec les battements par minute (BPM), qui servent de mesure standard de la fréquence cardiaque d’une personne. Une série temporelle multivariée se mesure elle aussi à intervalles définis, mais suit plusieurs indicateurs. Par exemple, un hôpital peut recueillir et suivre simultanément la fréquence cardiaque et le taux d’oxygène dans le sang d’un patient, car leur combinaison permet d’obtenir une vue plus complète de la circulation sanguine. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EIntervalles de temps réguliers ou irréguliers\u003C/h4\u003E\n\u003Cp\u003ELes valeurs enregistrées à intervalles réguliers sont relevées selon une cadence temporelle constante ; on peut par exemple relever la température dans une pièce toutes les minutes. Des événements distincts peuvent être enregistrés à intervalles irréguliers au fur et à mesure qu’ils se produisent, par exemple à chaque fois qu’un service cloud traite une requête. Ainsi, il peut y avoir plusieurs événements sur un court laps de temps ou aucun événement du tout. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ESéries temporelles stationnaires ou non\u003C/h4\u003E\n\u003Cp\u003ELes modèles de prévision de séries temporelles s’appuient sur des données stationnaires, auquel cas la plage de données est prévisible dans le temps et ne diffère pas de ses propriétés statistiques telles que la moyenne et la variance. Les données non stationnaires changent, souvent en fonction de la saisonnalité ou de toute autre volatilité. Les séries stationnaires sont plus fiables et plus faciles à analyser que les séries non stationnaires, même si des techniques statistiques comme la différenciation peuvent rendre des données non stationnaires suffisamment stables pour permettre des prévisions.\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"],":type":"snowflake-site/components/container"},"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","layout":"RESPONSIVE_GRID","columnCount":12,"id":"and","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-4bac06dc89","additionalClasses":"headline-decoration","type":"heading2","lines":["Intérêt de l’analyse de données de séries temporelles"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-57968b421e","text":"\u003Cp\u003ELes séries temporelles peuvent servir dans un large éventail de cas d’usage, certains axés sur l’atténuation des risques et la disponibilité des services, d’autres sur l’analyse approfondie des données historiques afin d’y découvrir des tendances utiles.\u003C/p\u003E\n\u003Cp\u003ECertains des cas d’usage les plus simples s’efforcent de définir une référence pour une activité, qui peut aller de la latence du service informatique au nombre d’engagements suscités chaque jour par des initiatives marketing. Dans ce cas, une série temporelle permet de détecter tout comportement anormal. Par exemple, un pic de latence aberrant peut signifier qu’une maintenance du réseau est nécessaire, tandis qu’une augmentation soudaine de l’engagement marketing peut fournir des informations précieuses sur les messages les plus performants.\u003C/p\u003E\n\u003Cp\u003EVous pouvez également utiliser des séries temporelles pour prendre des décisions éclairées. Prenons un exemple populaire : la gestion des stocks. En analysant les hausses et les baisses passées de la demande client, vous pouvez optimiser votre supply chain et votre calendrier de production pour vous assurer que vos produits et services peuvent s’adapter à la hausse pour répondre à la demande et à la baisse pour maximiser les économies.\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"],":type":"snowflake-site/components/container"},"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","layout":"RESPONSIVE_GRID","columnCount":12,"id":"how","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-e492e9769a","additionalClasses":"headline-decoration","type":"heading2","lines":["Méthodes et techniques d’analyse de séries temporelles"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-0733a5434e","text":"\u003Cp\u003EVous pouvez utiliser différentes méthodes pour tirer des informations utiles d’une analyse de séries temporelles. La méthode à choisir dépend des propriétés des séries temporelles et des données historiques disponibles. Voici quelques-unes des méthodes d’analyse de séries temporelles parmi les plus populaires.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EMoyennes mobiles et lissage exponentiel\u003C/h4\u003E\r\n\u003Cp\u003ECes deux méthodes utilisent des moyennes issues de données historiques pour réduire l’impact des fluctuations d’un indicateur. En supprimant le bruit et d’autres variations aberrantes, une moyenne mobile peut vous aider à découvrir dans des données une tendance générale au fil du temps. Par exemple, la moyenne mobile du cours d’une action capture la trajectoire globale de ce cours sur une période définie, plutôt que les hausses et les baisses imprévisibles. Le lissage exponentiel est une technique qui privilégie les données récentes par rapport aux données plus anciennes. Vous obtenez ainsi une moyenne qui intègre les changements récents afin de prévoir une tendance qui reflète l’état actuel des données. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003EModèles ARIMA et SARIMA\u003C/h4\u003E\r\n\u003Cp\u003ELa moyenne mobile intégrée autorégressive (ARIMA) combine trois techniques : l’autorégression, la différenciation et une moyenne mobile, afin de prédire des variables futures. En combinant ces tactiques, la méthode ARIMA peut utiliser une série temporelle non stationnaire pour fournir des prédictions précises, d’après une estimation de l’évolution future de la variable basée sur la moyenne mobile des données historiques. La moyenne mobile intégrée autorégressive saisonnière (SARIMA) prend en compte les variations saisonnières des données, afin de fournir des prévisions à partir de données qui présentent une saisonnalité.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003ELe machine learning au service des prévisions de séries temporelles\u003C/h4\u003E\r\n\u003Cp\u003EDe nombreuses entreprises intègrent des techniques de ML telles que des réseaux neuronaux récurrents ou des \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/decision-tree/\"\u003Emodèles d’arbres de décision\u003C/a\u003E dans leur processus d’analyse, car ces modèles peuvent générer des prévisions plus précises et ingérer beaucoup plus de données. En outre, le ML peut également automatiser les aspects chronophages et laborieux de l’analyse, de façon à gagner en efficacité organisationnelle.\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"],":type":"snowflake-site/components/container"},"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","layout":"RESPONSIVE_GRID","columnCount":12,"id":"why","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-118c533411","additionalClasses":"headline-decoration","type":"heading2","lines":["Liste de contrôle pour l’analyse de séries temporelles"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-66952e7983","text":"\u003Cp\u003EMême avec des outils puissants de machine learning, pour réussir à mettre en œuvre l’analyse de séries temporelles, vous devez définir minutieusement les objectifs et les limites de votre projet. Voici les cinq étapes à suivre pour lancer un nouveau projet d’analyse de séries temporelles.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1. Définition de l’objectif\u003C/h4\u003E\n\u003Cp\u003EIl est important d’avoir une idée précise de ce que vous espérez apprendre, non seulement pour évaluer la réussite de votre projet, mais aussi pour comprendre les limites de votre jeu de données.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2. Préparation et nettoyage des données\u003C/h4\u003E\n\u003Cp\u003EUne fois votre objectif clairement défini, collectez un jeu de données et utilisez des techniques de lissage pour le nettoyer et corriger les anomalies, les valeurs manquantes et tout autre problème dans le jeu de données.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E3. Visualisation et exploration des schémas\u003C/h4\u003E\n\u003Cp\u003ELa visualisation des données sous forme de graphiques vous aide à détecter les schémas courants, comme des tendances saisonnières ou générales. L’utilisation d’une fonction d’autocorrélation (ACF) peut vous aider à déterminer si le jeu de données est stationnaire ou non.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E4. Choix de la méthode appropriée\u003C/h4\u003E\n\u003Cp\u003ESelon les données et votre objectif, vous pouvez choisir d’appliquer les méthodes ARIMA ou SARIMA à des jeux de données s’il est pertinent de prendre en compte la saisonnalité par rapport à votre objectif particulier. Pour des jeux de données plus volumineux ou des analyses de données plus complexes, vous devrez peut-être utiliser une technique de ML ou de deep learning pour obtenir des résultats.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E5. Validation et interprétation des résultats\u003C/h4\u003E\n\u003Cp\u003EDe nombreuses techniques permettent de valider l’exactitude de vos résultats, généralement en comparant les prévisions avec les résultats réels au fur et à mesure. Une fois que vous avez validé que votre modèle prédit avec précision les résultats futurs, vous pouvez utiliser ces prévisions pour prendre des décisions afin d’atteindre vos objectifs définis à l’étape 1. Vous pouvez par exemple prévoir l’évolution de la demande des consommateurs au cours des six prochains mois.\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"],":type":"snowflake-site/components/container"},"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","layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-e96051845f","additionalClasses":"headline-decoration","type":"heading2","lines":["Exemples et cas d’usage de l’analyse de séries temporelles"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-819630d749","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EChaque secteur de l’économie moderne utilise l’analyse de données sous une forme ou une autre pour éclairer sa prise de décision. Voici quelques-uns des cas d’usage les plus courants de l’analyse de séries temporelles, par secteur d’activité :\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ERetail et e-commerce\u003C/h4\u003E\n\u003Cp\u003EL’analyse de séries temporelles est particulièrement utile dans le secteur du \u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/retail-consumer-goods/\"\u003Eretail\u003C/a\u003E, à la fois pour suivre l’intérêt des clients et l’activité d’achat, mais aussi pour garantir la disponibilité des services. Côté prévision, de nombreuses entreprises intègrent l’analyse de séries temporelles dans leurs décisions de gestion de la supply chain, en s’appuyant sur des données historiques pour éviter d’éventuelles perturbations et planifier les niveaux de stock en vue des promotions commerciales à venir.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ESanté et sciences de la vie\u003C/h4\u003E\n\u003Cp\u003ELes organisations du secteur de la \u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/healthcare-and-life-sciences/\"\u003Esanté\u003C/a\u003E et du bien-être utilisent beaucoup l’analyse de séries temporelles, car elle peut s’appliquer à tous les domaines, des électrocardiographes aux \u003Ca href=\"https://www.snowflake.com/en/customers/all-customers/case-study/whoop/\"\u003Etechnologies portables\u003C/a\u003E. Elle est également utile dans la recherche scientifique, puisque les chercheurs peuvent l’utiliser pour analyser de grands jeux de données associés à une région ou une population donnée, par exemple pour tester les eaux usées afin d’y détecter l’augmentation de certains virus.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EMarketing et analyse client\u003C/h4\u003E\n\u003Cp\u003EL’analyse de séries temporelles peut être un outil stratégique pour suivre les performances de différents types de campagnes \u003Ca href=\"https://www.snowflake.com/fr/solutions/departments/marketing/\"\u003Emarketing\u003C/a\u003E. Elle peut permettre, par exemple, d’établir un chiffre de référence concernant le nombre de nouveaux clients obtenus chaque semaine, afin de mesurer l’impact des nouveaux messages, des nouvelles publicités ou d’autres initiatives 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"],":type":"snowflake-site/components/container"},"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","layout":"RESPONSIVE_GRID","columnCount":12,"id":"ai","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-18602f3575","additionalClasses":"headline-decoration","type":"heading2","lines":["Avantages de l’analyse de séries temporelles pour la réussite des entreprises"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-c921084889","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EL’analyse de séries temporelles est populaire dans de nombreux secteurs, car elle permet aux entreprises de mieux comprendre le passé, de planifier l’avenir, de s’adapter à l’évolution des conditions du marché et d’améliorer les performances de leurs produits, le tout en se basant sur des données historiques. Analysons de plus près quelques-uns de ces avantages clés :\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1. Amélioration de la précision des prévisions\u003C/h4\u003E\n\u003Cp\u003EEn raison de sa structure, l’analyse de séries temporelles nécessite un nettoyage approfondi des données et des contrôles de stabilité, ce qui explique en partie son utilité pour les \u003Ca href=\"https://www.snowflake.com/fr/customers/all-customers/case-study/igs-energy/\"\u003Eprévisions\u003C/a\u003E. Si vous prenez en compte le bruit, les variables manquantes et d’autres anomalies potentielles et comparez les prévisions aux observations hors échantillon, vous pouvez créer et améliorer vos capacités de prévision.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2. Meilleure planification des ressources\u003C/h4\u003E\n\u003Cp\u003EL’analyse de séries temporelles vous fournit des prévisions précises sur l’évolution de la demande des consommateurs, les coûts et les inefficacités de la supply chain ou encore les besoins en calcul des systèmes informatiques. Ainsi, elle peut vous aider à minimiser le gaspillage sans nuire aux performances ni à la disponibilité de vos produits.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E3. Surveillance des performances en temps réel\u003C/h4\u003E\n\u003Cp\u003EEn plus de son puissant potentiel en matière de prévisions, l’analyse de séries temporelles peut être utilisée en temps réel pour détecter rapidement des problèmes tels que des pics de latence, des augmentations soudaines de la demande ou d’autres changements.\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"],":type":"snowflake-site/components/container"},"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","layout":"RESPONSIVE_GRID","columnCount":12,"id":"of","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-6632025ab1","additionalClasses":"headline-decoration","type":"heading2","lines":["Défis de l’analyse de séries temporelles"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-49cca2a7e2","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EL’analyse de séries temporelles n’est qu’un outil parmi d’autres pour analyser des données. Par conséquent, ce n’est pas toujours la méthode idéale pour chaque cas d’usage. En outre, pour être d’une quelconque utilité, l’analyse de séries temporelles doit disposer de données de qualité, traitées de manière appropriée pour garantir leur fiabilité, ce qui n’est pas toujours le cas. Voici quelques-uns des défis les plus courants soulevés par cette discipline.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EQualité des données et valeurs manquantes\u003C/h4\u003E\n\u003Cp\u003EVous aurez beau adopter une approche bien réfléchie, si vos données sont incohérentes ou comportent de nombreuses valeurs manquantes, la précision et l’utilité de votre modèle de séries temporelles s’en trouveront considérablement limitées.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EModèles non stationnaires et complexes\u003C/h4\u003E\n\u003Cp\u003ES’il est possible de rendre stationnaires des jeux de données grâce à des techniques comme la différenciation, cela peut soulever de nouveaux problèmes comme du bruit. Des modèles d’analyse de séries temporelles peuvent parfois avoir du mal à gérer des données complexes, comme un indicateur associé à plusieurs tendances saisonnières qui évoluent en fonction de l’heure, du jour de la semaine et du mois. Du fait de cette complexité, il peut être difficile de rendre les données stationnaires et de produire des prévisions précises.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ELimitations en matière d’évolutivité et de traitement en temps réel\u003C/h4\u003E\n\u003Cp\u003EComme les prévisions de séries temporelles dépendent de données historiques, il peut s’avérer fastidieux de régler, de tester et d’exécuter des modèles de séries temporelles en utilisant de grands jeux de données. Vos opérations peuvent alors atteindre les limites de votre architecture ou devenir coûteuses face à la croissance de votre jeu de données.\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"],":type":"snowflake-site/components/container"},"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","layout":"RESPONSIVE_GRID","columnCount":12,"id":"con","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-a39c5e7748","additionalClasses":"headline-decoration","type":"heading2","lines":["Conclusion"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-03f24b5461","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EL’analyse de séries temporelles est l’une des techniques d’analyse les plus précieuses à la disposition des entreprises. Elle permet de faire des choix éclairés basés sur des prévisions précises, ainsi que d’identifier des opportunités d’optimisation de l’efficacité structurelle et d’autres améliorations. Grâce au développement et à la puissance des outils de ML et d’IA, cette méthode est devenue plus efficace et accessible, permettant même à des équipes non techniques de créer des modèles pour mesurer leurs performances et améliorer leurs prévisions. Il n’est donc pas surprenant que l’analyse de séries temporelles reste l’une des principales méthodes d’analyse utilisées par les \u003Ca href=\"https://www.snowflake.com/fr/product/analytics/\"\u003Eplateformes analytiques modernes\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"],":type":"snowflake-site/components/container"},"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","layout":"RESPONSIVE_GRID","columnCount":12,"id":"time","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-445316591f","additionalClasses":"headline-decoration","type":"heading2","lines":["Questions fréquentes sur l’analyse de séries temporelles"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-54c07f0ed1","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EVoici quelques-unes des questions les plus couramment posées sur l’analyse de séries temporelles.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-c47c51ea2b","showDivider":false,"accordionItemsList":[{"title":"Quelle est la différence entre l’analyse de séries temporelles et la prévision de séries temporelles ?","richText":"\u003Cp\u003EL’analyse de séries temporelles consiste à analyser des données actuelles et historiques afin d’en extraire de nouvelles informations. Elle peut permettre par exemple de mesurer les performances d’une campagne marketing par rapport à des campagnes passées en se basant sur des indicateurs tels que les clics, les prospects qualifiés, etc. La prévision de séries temporelles suggère un modèle qui s’appuie sur des données historiques pour prévoir les valeurs futures de la série, par exemple en fournissant une \u003Ca href=\"https://www.snowflake.com/en/fundamentals/predictive-analytics-marketing/\"\u003Eprévision de l’impact potentiel d’une nouvelle campagne marketing\u003C/a\u003E d’après des performances passées. La prévision de séries temporelles est considérée comme une sous-discipline de l’analyse de séries temporelles.\u003C/p\u003E\n"},{"title":"Qu’est-ce qu’un modèle de prévision de séries temporelles ?","richText":"\u003Cp\u003EUn modèle de prévision est une méthode utilisée pour formuler des prévisions éclairées et précises des valeurs futures d’un jeu de données de séries temporelles. Le choix du bon modèle est essentiel pour obtenir des prévisions de séries temporelles de qualité. Pour choisir le plus approprié, les analystes doivent tenir compte de facteurs tels que la stationnarité et la complexité du jeu de données, ainsi que de leurs objectifs généraux.\u003C/p\u003E\n"},{"title":"Quelles sont les limites de l’analyse de séries temporelles ?","richText":"\u003Cp\u003EBien qu’il s’agisse d’un outil puissant, l’analyse de séries temporelles n’est pas l’outil idéal en toutes circonstances. De nombreuses méthodes basées sur des séries temporelles ont besoin de grandes quantités de données historiques pour fournir une analyse précise. De plus, certains modèles (comme ARIMA et SARIMA) nécessitent généralement un réglage important avant de parvenir à des prévisions précises. De nombreux modèles peinent également dans les cas complexes, comme face à des tendances saisonnières multiples. Vous pouvez résoudre certains de ces défis à l’aide d’outils de ML ou d’IA, mais le prétraitement de données à grande échelle peut alors entraîner des problèmes de surapprentissage ou d’autres erreurs. \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"],":type":"snowflake-site/components/container"},"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","layout":"RESPONSIVE_GRID","columnCount":12,"id":"using",":items":{"title_v2":{"id":"title-v2-5ecd626722","additionalClasses":"headline-decoration","type":"heading2","lines":["Clients qui utilisent Snowflake pour l’analytique "],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"flexible_column_cont":{"id":"flexible-column-container-c89ab75fa0","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":{"layout":"SIMPLE","id":"container-1fea9379c8","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"card_v2":{"id":"card-v2-bbb3359227","configurationStatus":{"configured":true,"message":""},"text":{"id":"text","text":"\u003Cp\u003EGrâce à Snowflake et Apache Iceberg, les équipes de WHOOP centralisent l’accès aux données tout en réduisant la complexité, en réduisant les coûts et en améliorant des processus stratégiques tels que le développement de fonctionnalités et les prévisions financières.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical",":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["WHOOP améliore les prévisions financières en matière d’IA et de ML tout en améliorant l’expérience de ses membres"],":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":"Lire le témoignage"},"image":{"id":"image","alt":"power digital logo","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--3c22294a-3e45-4a55-aa6d-f48810873b7e/whoop-customer-card%25402x.jpg?quality=85&preferwebp=true","lazyEnabled":true,"width":"1680","height":"720",":type":"snowflake-site/components/image"},"type":"content-card"}},":itemsOrder":["card_v2"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-d205955893","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"card_v2_copy":{"id":"card-v2-f053bc23ad","configurationStatus":{"configured":true,"message":""},"text":{"id":"text","text":"\u003Cp\u003EGrâce à Snowflake, IGS Energy utilise les données pour résoudre les cas d’usage d’IA et de ML (des modèles de prévision plus rentables à la détection plus précise des anomalies) afin d’atteindre son objectif d’un avenir durable pour tous.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical",":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["IGS Energy utilise l’IA et le ML pour simplifier les prévisions et la détection des anomalies"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/fr/customers/all-customers/case-study/igs-energy/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Lire le témoignage"},"image":{"id":"image","alt":"igs energy","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--eea8935e-a8ba-4d6c-a42c-1a40ff93d58e/igs-energy-customer-card%25402x.jpg?quality=85&preferwebp=true","lazyEnabled":true,"width":"1680","height":"720",":type":"snowflake-site/components/image"},"type":"content-card"}},":itemsOrder":["card_v2_copy"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["title_v2","flexible_column_cont"],":type":"snowflake-site/components/container"},"container_copy_copy__373061683":{"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","layout":"RESPONSIVE_GRID","columnCount":12,"id":"resources","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-a4565a8d19","type":"heading2","lines":["Ressources sur l’analyse de données"],":type":"snowflake-site/components/title-v2"},"flexible_column_cont":{"id":"flexible-column-container-baf75c0aff","type":"2-column-even","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-f85d5178ac","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"content_chip_copy_co":{"id":"content-chip-a88573d9df","tagText":"produit","tagColor":"#29B5E8","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/fr/product/analytics"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Explorer la solution"},"headline":{"id":"title","type":"heading5","lines":["Snowflake pour 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