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aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":items":{"title_v2":{"id":"title-v2-f614dacffe","type":"heading2","lines":["Übersicht"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-ff399699e9","text":"\u003Cp\u003EBei der Zeitreihenanalyse handelt es sich um eine Methode, die eine Variable in regelmäßigen Zeitabständen misst und es Ihnen ermöglicht, die Eigenschaften dieser Daten zu beobachten und zu analysieren. Mit ihr können Sie Kennzahlen im Zeitverlauf verfolgen und historische Daten auf Trends analysieren, was sie zu einem unverzichtbaren Tool in einer Reihe verschiedener Anwendungsfälle macht. Sie wird in zahlreichen Branchen eingesetzt, insbesondere in Branchen, die große Datenmengen erfassen, wie \u003Ca href=\"https://www.snowflake.com/de/solutions/industries/healthcare-and-life-sciences/\"\u003EGesundheitswesen\u003C/a\u003E, \u003Ca href=\"https://www.snowflake.com/de/blog/streamlining-financial-market-intelligence-time-series-innovations/\"\u003EFinanzen\u003C/a\u003E, \u003Ca href=\"https://www.snowflake.com/de/solutions/industries/technology/\"\u003ETechnologie\u003C/a\u003E und Logistik. \u003C/p\u003E\n\u003Cp\u003EDurch die Identifizierung historischer Trends und Korrelationen können Analyst:innen mithilfe von \u003Ca href=\"https://www.snowflake.com/en/blog/time-series-analytics-asof-join-generally-available/\"\u003EZeitreihenanalysen\u003C/a\u003E aussagekräftige Vorhersagen über Produktnachfrage oder Rechenanforderungen treffen. Das Wachstum bei der \u003Ca href=\"https://www.snowflake.com/de/product/features/data-ingestion/\"\u003EDatenerfassung\u003C/a\u003E, -verarbeitung und -speicherung in nahezu jedem Wirtschaftssektor hat die Leistungsfähigkeit dieser Technik erhöht, ebenso wie die Einführung von \u003Ca href=\"https://www.snowflake.com/de/product/ai/\"\u003Emaschinellem Lernen und KI\u003C/a\u003E. In diesem Artikel untersuchen wir die Details der Zeitreihenanalyse, was sie nützlich macht und wie Sie sie zur Verbesserung der Geschäftsergebnisse einsetzen können. \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","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"a",":items":{"title_v2":{"id":"title-v2-a25f79e7da","additionalClasses":"headline-decoration","type":"heading2","lines":["Was ist Zeitreihenanalyse?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-a9fb69d28a","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EDie Analyse von Zeitreihen klingt täuschend einfach, was über ihre Flexibilität und potenzielle Komplexität hinwegtäuscht. Alle Zeitreihendaten werden als geordnete Erfassung von Daten dargestellt, die im Zeitverlauf in gleichmäßigen Abständen erfasst werden. Ein Beispiel ist die Anzahl der Newsletter-Anmeldungen, die ein Unternehmen monatlich über einen Zeitraum von einem Jahr erhält. Sie kann besonders wirkungsvoll sein, wenn sie auf historische Daten angewendet wird, da Sie durch die Verlängerung des Zeitrahmens der Reihe langfristige Trends beobachten und beispielsweise die Auswirkungen von sich verändernden wirtschaftlichen Gegenwinden, behördlichen Veränderungen oder Lieferkettenstörungen abschätzen können. \u003C/p\u003E\n\u003Cp\u003EDas entscheidende Merkmal dieser Technik ist – wie zu erwarten – der konstante Trommelschlag der Zeit – im Gegensatz zu Techniken wie der Regressionsanalyse, bei der die Beziehung zwischen Variablen untersucht wird, oder der Querschnittsanalyse, bei der verschiedene Datentypen zu einem einzigen Zeitpunkt betrachtet werden.\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","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","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"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":items":{"container_copy_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"is",":items":{"title_v2":{"id":"title-v2-0c6a169ce9","additionalClasses":"headline-decoration","type":"heading2","lines":["Komponenten von Zeitreihendaten"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-5c7629dc8f","text":"\u003Cp\u003EDie Daten, mit denen Sie die Zeitreihe erstellen, können bestimmte Merkmale aufweisen, die sich in folgende Elemente unterteilen lassen:\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ETrend\u003C/h4\u003E\n\u003Cp\u003EDer Trend beschreibt die Richtung, in die sich die Kennzahl bewegt, sofern vorhanden. Um unser vorheriges Beispiel fortzusetzen, könnten Sie feststellen, dass Ihre Newsletter-Anmeldungen von Monat zu Monat wachsen, was bedeuten würde, dass der Trend bei den Anmeldungen steigt.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ESaisonalität\u003C/h4\u003E\n\u003Cp\u003ESaisonalität beschreibt ein wiederkehrendes Muster in den Daten, das in einem bestimmten Zyklus auftritt und in der Regel an ein vorhersehbares und konsistentes Ereignis gebunden ist. Eines der häufigsten Beispiele ist der Anstieg der Verbraucherausgaben in der Weihnachtszeit, aber ein detaillierteres Beispiel könnte der plötzliche Anstieg der Wohnungen sein, die am 1. und 15. eines jeden Monats vermietet werden können.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EZyklische Muster\u003C/h4\u003E\n\u003Cp\u003EZyklen beschreiben langfristige Muster, die nicht durch eine bestimmte Jahreszeit oder ein bestimmtes Ereignis definiert sind. Zyklen beziehen sich oft auf wirtschaftliche Expansion und Kontraktion, was in der Regel über lange Zeiträume geschieht und nicht an ein Ereignis oder Vorkommnis gebunden ist. Beispiele wären die Verringerung der Ausgaben für Unternehmenssoftware während einer Rezession und die Umkehrung während einer Phase des Wirtschaftswachstums.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EUnregelmäßiges oder zufälliges Rauschen\u003C/h4\u003E\n\u003Cp\u003ERauschen beschreibt Datenpunkte, die durch keine der anderen Elemente erklärt werden können, wie z. B. ein technisches Problem, das eine Abweichung von der Norm verursacht, ein einmaliges Ereignis oder ein anderes undefiniertes Ereignis. Sensorfehler in Maschinenanlagen oder kleine Schwankungen der Lagerpreise von Minute zu Minute sind gute Beispiele für Rauschen.\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","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":items":{"title_v2":{"id":"title-v2-58ca76022d","additionalClasses":"headline-decoration","type":"heading2","lines":["Arten von Zeitreihendaten"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-37e4044a50","text":"\u003Cp\u003EZeitreihendaten können in verschiedene Arten eingeteilt werden, basierend auf der Anzahl der Variablen, der Art und Weise, wie die Daten erfasst werden, oder anderen statistischen Informationen über die Zeitreihe. Im Folgenden stellen wir drei wichtige Kategorisierungsmethoden für Zeitreihendaten vor.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EUnivariate vs. multivariate Zeitreihen\u003C/h4\u003E\n\u003Cp\u003EEine univariate Zeitreihe verfolgt eine einzelne Kennzahl in festgelegten Abständen. Ein gängiges Beispiel wären Schläge pro Minute (BPM), die das Standardmaß für die Herzfrequenz einer Person sind. Eine multivariate Zeitreihe verwendet dasselbe Intervall, verfolgt aber mehrere Metriken über den Zeitraum. Ein Krankenhaus kann beispielsweise die Herzfrequenz und den Blutsauerstoffgehalt eines Patienten gleichzeitig erfassen und verfolgen, da eine Kombination dieser beiden einen umfassenderen Überblick über die Kreislaufgesundheit bietet. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ERegelmäßige vs. unregelmäßige Zeitintervalle\u003C/h4\u003E\n\u003Cp\u003ERegelmäßige Intervalle sind ein Wert, der in einem konstanten Zeittakt aufgezeichnet wird; beispielsweise die Temperatur in einem Raum, die einmal pro Minute aufgezeichnet wird. Ein unregelmäßiges Zeitintervall zeichnet einzelne Ereignisse auf, wie z. B. die Nachverfolgung jedes Mal, wenn ein Cloud-Dienst eine Anfrage verarbeitet, die sich in einem kleinen Fenster mehrfach oder gar nicht clustern kann. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EStationäre vs. nichtstationäre Zeitreihen\u003C/h4\u003E\n\u003Cp\u003EZeitreihenprognosemodelle basieren auf stationären Daten, wobei der Datenbereich zeitlich vorhersehbar ist und nicht von seinen statistischen Eigenschaften wie Mittelwert und Varianz abweicht. Instationäre Daten ändern sich, oft saisonal oder aufgrund anderer Volatilität. Stationäre Datenreihen sind zuverlässiger und einfacher zu analysieren als nichtstationäre, obwohl statistische Methoden wie die Differenzenbildung nichtstationäre Daten stabil genug für Prognosen machen können.\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","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_259718203":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"and",":items":{"title_v2":{"id":"title-v2-1435fb5235","additionalClasses":"headline-decoration","type":"heading2","lines":["Warum sollten Sie die Datenanalyse von Zeitreihen nutzen?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-d0a663e609","text":"\u003Cp\u003ESie können Zeitreihen in einer Vielzahl verschiedener Kapazitäten nutzen, wobei sich einige auf Risikominderung und Serviceverfügbarkeit konzentrieren und andere auf die tiefgreifende Analyse historischer Daten, um nützliche Muster aufzudecken.\u003C/p\u003E\n\u003Cp\u003EEinige der einfacheren Anwendungsfälle konzentrieren sich auf die Festlegung einer Baseline für eine Aktivität, die von der Latenzzeit von IT-Diensten bis hin zur Anzahl der Interaktionen, die Ihre Marketingaktivitäten täglich erzielen, reichen kann. Mit einer Zeitreihe können Sie diese Fälle auf anomales Verhalten überwachen. Ein Beispiel dafür ist eine plötzliche Zunahme der Latenzzeit, was bedeuten kann, dass ein Netzwerk gewartet werden muss. Gleichzeitig könnte ein plötzlicher Anstieg des Marketingengagements wertvolle Erkenntnisse darüber liefern, welche Botschaften am besten funktionieren.\u003C/p\u003E\n\u003Cp\u003ESie können auch Zeitreihen nutzen, um fundierte Entscheidungen zu treffen. Ein beliebtes Beispiel hierfür ist das Bestandsmanagement. Durch die Analyse historischer Zunahmen und Abnahmen der Kundennachfrage können Sie Ihre Lieferkette und Ihren Produktionsplan optimieren, um sicherzustellen, dass Ihre Produkte und Dienstleistungen je nach Nachfrage hochskaliert und herunterskaliert werden können, um Kosteneinsparungen zu maximieren.\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","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__987904085":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"how",":items":{"title_v2":{"id":"title-v2-f7ad7902df","additionalClasses":"headline-decoration","type":"heading2","lines":["Methoden und Techniken zur Zeitreihenanalyse"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-22ed5ff8d2","text":"\u003Cp\u003ESie können verschiedene Methoden nutzen, um nützliche Erkenntnisse aus einer Zeitreihenanalyse zu gewinnen. Für welche Methode Sie sich entscheiden, hängt von den Eigenschaften der Zeitreihe und der Verfügbarkeit historischer Daten ab. Hier einige der beliebtesten Zeitreihenanalysemethoden.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EGleitender Durchschnitt und exponentielle Glättung\u003C/h4\u003E\n\u003Cp\u003EBeide Methoden nutzen Durchschnittswerte historischer Daten, um die Auswirkungen von Schwankungen in einer Kennzahl zu reduzieren. Ein gleitender Durchschnitt kann Ihnen helfen, einen allgemeinen Datentrend im Laufe der Zeit aufzudecken, indem er Rauschen und andere aberrante Änderungen entfernt. Der gleitende Durchschnitt eines Aktienkurses erfasst die Gesamtkurve dieses Kurses über einen bestimmten Zeitraum, anstatt sich unvorhersehbar nach oben oder unten zu verändern. Exponentielle Glättung ist eine Technik, die aktuelle Daten gegenüber älteren bevorzugt. So erhalten Sie einen Durchschnitt, der aktuelle Änderungen berücksichtigt und einen Trend prognostizieren kann, der den aktuellen Stand der Daten widerspiegelt. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EARIMA- und SARIMA-Modelle\u003C/h4\u003E\n\u003Cp\u003EAutoregressiver integrierter gleitender Durchschnitt (Autoregression, Differenzenbildung und gleitender Durchschnitt) kombiniert drei Techniken, um künftige Variablen vorherzusagen. Durch die Kombination dieser Taktiken kann ARIMA eine instationäre Zeitreihe nutzen, um genaue Vorhersagen zu treffen und die Art und Weise zu schätzen, wie sich die Variable auf der Grundlage des gleitenden Durchschnitts historischer Daten verändern wird. SARIMA (saisonaler autoregressiver integrierter gleitender Durchschnitt) berücksichtigt saisonale Änderungen in den Daten, sodass Sie Prognosen anhand von Daten erstellen können, die die Saisonalität zeigen.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EMaschinelles Lernen für Zeitreihenprognosen\u003C/h4\u003E\n\u003Cp\u003EViele Unternehmen integrieren ML-Techniken wie rekurrente neuronale Netze oder Entscheidungsbaummodelle in ihren Analyseprozess, da diese Modelle genauere Prognosen erstellen und viel größere Datenmengen erfassen können. Darüber hinaus können sie die zeit- und arbeitsintensiven Aspekte der Analyse automatisieren und so die organisatorische Effizienz steigern.\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","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1985496925":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"why",":items":{"title_v2":{"id":"title-v2-dc8093da1a","additionalClasses":"headline-decoration","type":"heading2","lines":["Checkliste zur Zeitreihenanalyse"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-f34ca23f5e","text":"\u003Cp\u003ESelbst mit leistungsstarken ML-Tools bedeutet die erfolgreiche Implementierung einer Zeitreihenanalyse eine sorgfältige Abwägung der Ziele und Grenzen Ihres Projekts. Im Folgenden haben wir fünf Schritte zusammengefasst, die Sie ergreifen sollten, wenn Sie mit einem neuen Zeitreihenanalyseprojekt beginnen.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1. Ziel definieren\u003C/h4\u003E\n\u003Cp\u003EEine klare Vorstellung davon zu haben, was Sie lernen wollen, ist nicht nur wichtig, um den Erfolg zu bewerten, sondern auch, um die Grenzen Ihres Datasets zu verstehen.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2. Daten vorbereiten und bereinigen\u003C/h4\u003E\n\u003Cp\u003ESobald Ihr Ziel klar ist, erfassen Sie ein Dataset und wenden Sie Glättungstechniken an, um es zu bereinigen. So können Sie Ausreißer, fehlende Datenwerte und andere Probleme im Dataset beheben.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E3. Muster visualisieren und untersuchen\u003C/h4\u003E\n\u003Cp\u003EIndem Sie die Daten in grafischer Form visualisieren, können Sie alle gängigen Muster erkennen, wie z. B. Saisonalität oder allgemeine Trends. Mithilfe einer Autokorrelationsfunktion (ACF) können Sie feststellen, ob das Dataset stationär ist oder nicht.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E4. Die richtige Methode wählen\u003C/h4\u003E\n\u003Cp\u003EJe nach Daten und Ziel können Sie ARIMA oder SARIMA auf Datasets anwenden, bei denen die Saisonalität für Ihr jeweiliges Ziel relevant ist. Für größere Datasets oder komplexere Datenanalysen benötigen Sie möglicherweise eine ML- oder Deep-Learning-Technik, um Ergebnisse zu erzielen.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E5. Ergebnisse validieren und interpretieren\u003C/h4\u003E\n\u003Cp\u003EZahlreiche Techniken ermöglichen es Ihnen, zu validieren, ob Ihre Ergebnisse korrekt sind. In der Regel werden die Ergebnisse der Prognose mit den tatsächlichen Ergebnissen verglichen. Sobald Sie überprüft haben, ob Ihr Modell zukünftige Ergebnisse genau vorhersagt, können Sie anhand der Prognose Entscheidungen über Ihre in Schritt 1 beschriebenen Ziele treffen. Zum Beispiel Prognosen darüber, wie sich die Verbrauchernachfrage in den nächsten sechs Monaten verändern könnte.\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","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__127842237":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer",":items":{"title_v2":{"id":"title-v2-c264db610a","additionalClasses":"headline-decoration","type":"heading2","lines":["Beispiele und Anwendungsfälle für die Zeitreihenanalyse"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-b1df829bdd","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EJeder Sektor der modernen Wirtschaft nutzt Datenanalysen, um fundierte Entscheidungen zu treffen. Hier einige der häufigsten industriellen Anwendungsfälle für die Zeitreihenanalyse:\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EHandel und E-Commerce\u003C/h4\u003E\n\u003Cp\u003EDie Zeitreihenanalyse sieht in \u003Ca href=\"https://www.snowflake.com/de/solutions/industries/retail-consumer-goods/\"\u003EHandelskontexten\u003C/a\u003E einen erheblichen Nutzen, sowohl für die Nachverfolgung von Kundeninteresse und Kaufaktivität als auch für die Gewährleistung von Serviceverfügbarkeit. Auf der Prognoseseite integrieren viele Unternehmen Zeitreihenanalysen in ihre Entscheidungen im Supply-Chain-Management, indem sie historische Daten nutzen, um mögliche Versorgungsunterbrechungen zu umgehen und Lagerbestände für anstehende Verkaufsaktionen zu planen.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EGesundheitswesen und Life Sciences\u003C/h4\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/solutions/industries/healthcare-and-life-sciences/\"\u003EGesundheits-\u003C/a\u003E und Wellnessunternehmen nutzen die Zeitreihenanalyse umfassend, da sie sich auf alles anwenden lässt, von EKG-Geräten bis hin zu \u003Ca href=\"https://www.snowflake.com/en/customers/all-customers/case-study/whoop/\"\u003EWearables\u003C/a\u003E. Auch in der wissenschaftlichen Forschung können Forschende damit große Datasets aus einer Region oder einer festen Population analysieren, z. B. Abwassertests auf Zunahmen bestimmter Viren.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EMarketing und Kundenanalytik\u003C/h4\u003E\n\u003Cp\u003EDie Analyse von Zeitreihen kann ein wichtiges Werkzeug sein, um die Performance verschiedener Arten von \u003Ca href=\"https://www.snowflake.com/de/solutions/departments/marketing/\"\u003EMarketingkampagnen\u003C/a\u003E zu verfolgen. So können Sie beispielsweise eine Baseline festlegen, wie viele neue Kund:innen Sie jede Woche erhalten. Diese Baseline kann als Vergleichswert verwendet werden, um die Auswirkungen neuer Botschaften, Anzeigen oder anderer Marketingmaßnahmen zu messen.\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","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1720648998":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"ai",":items":{"title_v2":{"id":"title-v2-72347a5f94","additionalClasses":"headline-decoration","type":"heading2","lines":["Vorteile der Zeitreihenanalyse für den Geschäftserfolg"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-15236566a2","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EZeitreihenanalysen sind in vielen Branchen beliebt, da sie es Unternehmen ermöglichen, die Vergangenheit besser zu verstehen, für die Zukunft zu planen, sich auf veränderte Marktbedingungen einzustellen und die Produktleistung zu verbessern – alles auf der Grundlage historischer Daten. Hier einige wichtige Vorteile im Detail:\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1. Verbesserte Prognosegenauigkeit\u003C/h4\u003E\n\u003Cp\u003EDie Struktur der Zeitreihenmethode erfordert umfassende Datenbereinigungen und Stabilitätsprüfungen, was sie zu einem so nützlichen Modell für \u003Ca href=\"https://www.snowflake.com/de/customers/all-customers/case-study/igs-energy/\"\u003EPrognosen\u003C/a\u003E macht. Indem Sie Rauschen, fehlende Variablen und andere potenzielle Ausreißer berücksichtigen und Prognosen anhand von Beobachtungen außerhalb der Stichprobe testen, können Sie Ihre Prognosefunktionen verbessern.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2. Bessere Ressourcenplanung\u003C/h4\u003E\n\u003Cp\u003EDurch die genaue Prognose von Änderungen der Verbrauchernachfrage, Lieferkettenkosten und Ineffizienzen oder des Rechenaufwands von IT-Systemen kann die Zeitreihenanalyse Ihnen dabei helfen, Verschwendung zu minimieren, ohne die Produktleistung und -verfügbarkeit zu beeinträchtigen.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E3. Echtzeit-Performance-Überwachung\u003C/h4\u003E\n\u003Cp\u003ENeben leistungsstarkem Prognosepotenzial können Sie Zeitreihenanalysen für Echtzeitanwendungen nutzen, um Probleme wie Latenzspitzen, plötzliche Nachfragesteigerungen oder andere Änderungen schnell zu erkennen.\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","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__643019817":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"of",":items":{"title_v2":{"id":"title-v2-fce716225d","additionalClasses":"headline-decoration","type":"heading2","lines":["Herausforderungen der Zeitreihenanalyse"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-0769973fb2","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EDie Zeitreihenanalyse ist nur ein Tool im Werkzeugkasten der Datenanalyse – und daher möglicherweise nicht für jeden einzelnen Anwendungsfall die perfekte Methode. Darüber hinaus beruht ihr Nutzen auf Qualitätsdaten, die aus Zuverlässigkeitsgründen erfolgreich verarbeitet werden können und die möglicherweise nicht immer verfügbar sind. Hier einige der häufigsten Herausforderungen in dieser Disziplin.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EDatenqualität und fehlende Werte\u003C/h4\u003E\n\u003Cp\u003EUnabhängig davon, wie durchdacht Ihr Ansatz ist: Wenn Ihre Daten inkonsistent sind oder eine große Anzahl fehlender Werte aufweisen, wird die Genauigkeit und Brauchbarkeit Ihres Zeitreihenmodells erheblich reduziert.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EInstationarität und komplexe Muster\u003C/h4\u003E\n\u003Cp\u003EZwar können Sie Datasets mit Methoden wie Differenzenbildung stationär ausführen, doch das kann zu neuen Problemen wie Rauschen führen. Zeitreihenanalysemodelle können manchmal mit komplexen Daten zu kämpfen haben – beispielsweise mit Metriken mit mehreren saisonalen Mustern, die sich je nach Stunde, Wochentag und Monat ändern. Diese Komplexität kann es schwierig machen, die Daten stationär bereitzustellen und eine genaue Prognose zu erstellen.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ESkalierbarkeit und Einschränkungen bei der Echtzeitverarbeitung\u003C/h4\u003E\n\u003Cp\u003EDa Zeitreihenprognosen von historischen Daten abhängen, kann es umständlich sein, Zeitreihenmodelle mithilfe großer Datasets anzupassen, zu testen und auszuführen. Das kann bedeuten, dass Ihre Operationen an architektonische Grenzen stoßen oder mit zunehmender Größe des Datasets teuer in der Ausführung werden.\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","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__749217882":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"con",":items":{"title_v2":{"id":"title-v2-7d24c1469a","additionalClasses":"headline-decoration","type":"heading2","lines":["Fazit"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-a35ef4f9af","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EDie Zeitreihenanalyse ist eine der wichtigsten Analysemethoden, die Unternehmen zur Verfügung stehen. Sie ermöglicht es Ihnen, fundierte Entscheidungen auf der Grundlage präziser Prognosen zu treffen und Möglichkeiten für strukturelle Effizienz und Verbesserung zu ermitteln. Das Wachstum und die Leistungsfähigkeit von ML- und KI-Tools haben diese Methode effizienter und zugänglicher gemacht, sodass auch Teams ohne technische Vorkenntnisse Modelle entwickeln können, um die Performance zu messen und Prognosen zu verbessern. Es ist kaum überraschend, dass die Zeitreihenanalyse nach wie vor eine der wichtigsten analytischen Methoden \u003Ca href=\"https://www.snowflake.com/de/product/analytics/\"\u003Emoderner Analytics-Plattformen\u003C/a\u003E ist.\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","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__493640578":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","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"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"time",":items":{"title_v2":{"id":"title-v2-d7fda84550","additionalClasses":"headline-decoration","type":"heading2","lines":["FAQ zur Zeitreihenanalyse"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-86bb2d4afb","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EIm Folgenden haben wir einige der häufigsten Fragen zur Zeitreihenanalyse zusammengefasst.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-ba6d270a28","showDivider":false,"accordionItemsList":[{"title":"Was ist der Unterschied zwischen Zeitreihenanalyse und Zeitreihenprognose?","richText":"\u003Cp\u003EZeitreihenanalyse bezieht sich auf die Analyse aktueller und historischer Daten, um daraus neue Erkenntnisse zu gewinnen. So wird beispielsweise die Performance einer Marketingkampagne im Vergleich zu früheren Kampagnen anhand von Metriken wie Klickverhalten, qualifizierten Leads und anderen gemessen. Bei Zeitreihenprognosen wird ein Modell vorgeschlagen, das anhand historischer Daten künftige Werte in der Serie prognostiziert. So können Sie beispielsweise \u003Ca href=\"https://www.snowflake.com/en/fundamentals/predictive-analytics-marketing/\"\u003Evorhersagen, welche Auswirkungen eine potenzielle neue Marketingkampagne\u003C/a\u003E haben könnte, basierend auf der vergangenen Performance. Zeitreihenprognosen werden als Teilmenge der Zeitreihenanalyse betrachtet.\u003C/p\u003E\n"},{"title":"Was ist ein Zeitreihenmodell der Prognose?","richText":"\u003Cp\u003EEin Prognosemodell ist eine Methode, die verwendet wird, um fundierte und genaue Prognosen zukünftiger Werte in einem Zeitreihen-Dataset zu treffen. Die Auswahl des richtigen Modells ist eine entscheidende Komponente für erfolgreiche Zeitreihenprognosen. Analyst:innen werden Faktoren wie Dataset-Stationarität, Komplexität und ihre übergeordneten Ziele berücksichtigen, um das beste auszuwählen.\u003C/p\u003E\n"},{"title":"Welche Einschränkungen gibt es bei der Zeitreihenanalyse?","richText":"\u003Cp\u003EObwohl es sich um ein leistungsstarkes Tool handelt, ist die Zeitreihenanalyse möglicherweise nicht für jede Situation das ideale Tool. Viele Zeitreihenmethoden benötigen große Mengen historischer Daten, um genaue Analysen bereitzustellen, und Modelle wie ARIMA und SARIMA erfordern in der Regel erhebliche Anpassungen, um genaue Vorhersagen zu treffen. Viele Modelle kämpfen auch mit Komplexität, wie z. B. mehreren saisonalen Trends. Einige dieser Probleme können Sie mit ML- oder KI-Tools lösen, aber der Umfang der Datenvorverarbeitung kann dann zu Überanpassung oder anderen Fehlern führen. \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","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1190438074":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"using",":items":{"title_v2":{"id":"title-v2-aebcdfc9e6","additionalClasses":"headline-decoration","type":"heading2","lines":["Kunden, die Snowflake für Analytics nutzen "],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"flexible_column_cont":{"id":"flexible-column-container-d369ad39d5","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-cd233c2f7a",":items":{"card_v2":{"id":"card-v2-0ad89585ac","configurationStatus":{"configured":true,"message":""},"text":{"id":"text","text":"\u003Cp\u003EMit Snowflake und Apache Iceberg konnten die WHOOP-Teams den Datenzugriff zentralisieren, gleichzeitig die Komplexität reduzieren, Kosten senken und kritische Prozesse wie Finanzprognosen und die Entwicklung von Features verbessern.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical","title":{"id":"title","type":"heading4","lines":["WHOOP Improves AI/ML Financial Forecasting While Enhancing Members’ Experiences"],":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":"Customer Story lesen (Englisch)"},"image":{"id":"image","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--15863a57-5aeb-4400-b348-edd4aedb6520/whoop-customer-card%25402x.jpg?quality=85&preferwebp=true","alt":"power digital logo","height":"720","width":"1680",":type":"snowflake-site/components/image"},"type":"content-card",":type":"snowflake-site/components/card-v2"}},":itemsOrder":["card_v2"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-9f9252026b",":items":{"card_v2_copy":{"id":"card-v2-991d101741","configurationStatus":{"configured":true,"message":""},"text":{"id":"text","text":"\u003Cp\u003EMit Snowflake kann IGS Energy Daten nutzen, um KI-/ML-Anwendungsfälle zu unterstützen – von kostengünstigeren Prognosemodellen bis hin zu präziserer Anomalieerkennung. 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