Zurück zur englischen Version der DokumentationVerwenden Sie die IBM Granite und die Prognose-API zur Vorhersage zukünftiger Werte (Beta)
Verwenden Sie die IBM Granite und die Prognose-API zur Vorhersage zukünftiger Werte (Beta)
Letzte Aktualisierung: 16. Jan. 2025
Verwenden Sie die Zeitreihenprognose-API und die IBM Granite, die unter watsonx.ai verfügbar sind, um zukünftige Werte auf der Grundlage historischer Daten zu prognostizieren.
Wichtig: Die Zeitreihenvorhersagemethode der watsonx.ai API ist als Beta-Funktion verfügbar.
API-Referenz
Einzelheiten finden Sie in der API-Referenzdokumentation.
Python-SDK
Mit der Python watsonx.ai können Sie Code schreiben, der die Zeitreihenmodelle zur Vorhersage von Daten verwendet. Weitere Informationen finden Sie unter den folgenden Verweisen:
Beispielnotebook
Verwendung von Time Series Foundation-Modellen und Zeitreihendaten zur Vorhersage des Energiebedarfs
Zeitreihenprognosen
Verwenden Sie die Zeitreihen-API, um historische Datenbeobachtungen an ein foundation model zu übergeben, das zukünftige Werte mit Zero-Shot-Inferencing prognostizieren kann. Mit dieser Methode können Sie beispielsweise zukünftige Werte auf der Grundlage historischer Beobachtungen aus den folgenden Datentypen prognostizieren:
- Aktienkurse und Handelsvolumen
- Elektrokardiogramm (EKG)-Daten oder Polysomnogramm (PSG)-Aufzeichnungen
- Temperaturdaten oder seismografische Daten
- Netzleistungsmessungen
Unterstützte Stiftungsmodelle
IBM ' Granite Zeitreihen-Grundmodelle mit den folgenden Modell-IDs stehen für Prognosen in ' watsonx.ai: zur Verfügung
ibm/granite-ttm-512-96-r2: Erfordert mindestens 512 Datenpunkte pro Datensatz.ibm/granite-ttm-1024-96-r2: Erfordert mindestens 1.024 Datenpunkte pro Datensatz.ibm/granite-ttm-1536-96-r2: Erfordert mindestens 1.536 Datenpunkte pro Datensatz.
Die Granite arbeiten am besten mit Datenpunkten in Minuten- oder Stundenintervallen und erzeugen einen Prognosedatensatz mit 96 Datenpunkten pro Zielspalte. Die besten Ergebnisse erzielen Sie, wenn Sie das Modell verwenden, das auf der Grundlage der Ihnen zur Verfügung stehenden Daten die meisten Datenpunkte erfasst.
Weitere Informationen finden Sie auf der Musterkarte.
Um programmatisch eine Liste der verfügbaren Zeitreihenmodelle zu erhalten, können Sie die Methode List the available foundation models in der watsonx.ai as a service API verwenden. Geben Sie den Parameter " filters=function_time_series_forecast an, um nur die verfügbaren Zeitreihenmodelle zurückzugeben.
Beispiel:
curl -X GET \
'{url}/ml/v1/foundation_model_specs?version=2024-11-14&filters=function_time_series_forecast'
Ersetzen Sie im Beispiel die Variable " {url} durch den richtigen Wert für Ihren Fall, z. B. " us-south.ml.cloud.ibm.com.
Datenanforderungen
Eine Zeitreihe ist eine Sammlung von Datenpunkten, die im Laufe der Zeit erhoben werden.
Um die Zeitreihenmodelle Granite für die Vorhersage zukünftiger Werte nutzen zu können, müssen die Daten, die Sie zur Analyse einreichen, die folgenden Anforderungen erfüllen:
Die aufgezeichneten Daten müssen numerisch sein, z. B. Temperaturen oder Aktienkurse.
Ihre Daten müssen genügend Datenpunkte mit historischem Kontext enthalten, damit das Modell eine Vorhersage treffen kann. Für jedes foundation model ist eine Mindestanzahl von Datenpunkten pro Datensatz erforderlich. Wenn Sie mehr als die erforderliche Anzahl angeben, verwendet das Modell die jüngsten Datenpunkte bis zur Modellanforderung und ignoriert den Rest.
Jeder Datensatz muss die gleiche Anzahl von Elementen in den Arrays enthalten, die für die im Anfragekörper enthaltenen "
date, "id_columnsund "target_columns" angegeben sind. Sie können keinen Datenpunkt auslassen oder 'null' als Datenpunkt angeben. Überprüfen Sie Ihre Daten, bevor Sie den Antrag stellen.Obwohl der Dienst die meisten gängigen Datums- und Zeitformate akzeptiert, vermeiden Sie Verwirrung, die durch unterschiedliche Konventionen bei der Datumsformatierung entstehen kann, wenn Sie Daten mit Zeitstempeln im ISO 8601-Format (
2024-11-12T15:06:35) angeben. Bedeutet zum Beispiel 11/12/2024 den 12. November oder den 11. Dezember? Geben Sie die Daten im Format ISO 8601 mit dem Offset der koordinierten Weltzeit (UTC) (2024-11-12T15:06:35+0000) an, um offensichtliche doppelte oder fehlende Zeitstempel in den generierten Vorhersageergebnissen zu vermeiden.Nehmen Sie die Daten mit einer einheitlichen Häufigkeit auf. So können die Daten beispielsweise in Schritten von 1 Minute, 1 Stunde oder 1 Tag beobachtet werden. Wenn Ihre Zeitstempel nicht einheitlich sind, wird kein Fehler erzeugt, aber die Qualität Ihrer Ergebnisse könnte schlecht sein. Die erzeugten Prognosedaten werden auch mit der Häufigkeit formatiert, die Sie mit dem Parameter "
freqangeben.Wenn Sie z. B. eine Häufigkeit von einem Tag angeben (
"freq":"1D"), sehen Ihre Zeitstempel wahrscheinlich etwa so aus:"date": [ "2024-11-15T15:06:35", "2024-11-16T15:06:35", ... ]Wenn die Häufigkeit 5 Minuten beträgt (
"freq":"5min"), liegt jeder Datumszeitstempel wahrscheinlich 5 Minuten auseinander."date": [ "2024-11-15T15:06:35", "2024-11-15T15:11:35", ... ]Unterstützte Werte für Datums- und Zeitabkürzungen im Parameter frequency finden Sie unter Period aliases in der Dokumentation der Pandas-Bibliothek.
Wenn Sie Datenpunkte aus mehr als einer Quelle in die Analyse einbeziehen, muss jeder Datenpunkt einen eindeutigen Bezeichner enthalten, der angibt, zu welchem Datensatz er gehört.
Wenn Sie z. B. Einkaufstrends analysieren, könnten Sie an den Verkaufszahlen mehrerer Geschäfte während der Weihnachtszeit interessiert sein. Die folgende Tabelle zeigt zwei Datensätze, A und B. Datensatz A zeigt den Umsatz pro Tag für Standort A und Datensatz B zeigt den Umsatz pro Tag für Standort B.
Tabelle 1. Beispielhafte Verkaufsdaten Datum Dataset Verkäufe insgesamt (USD) 7. Dezember 2023 A $24,988 7. Dezember 2023 B $63,788 8. Dezember 2023 A $41,855 8. Dezember 2023 B $105,678 ... ... ... Obwohl die Tabelle Beobachtungen von zwei Tagen zeigt, muss Ihre Anfrage je nach dem von Ihnen verwendeten Modell zwischen 512 und 1.536 Datenpunkte enthalten. Wenn die Daten aus der Tabelle an die API übermittelt werden, werden sie wie folgt formatiert:
... "data": { "date": [ "2024-12-07T00:00:00", "2024-12-07T00:00:00", "2024-12-08T00:00:00", "2024-12-08T00:00:00", ... ], "ids": [ "A", "B", "A", "B", ... ], "sales-usd": [ 24988, 63788, 41855, 105678, ... ] }Sie können Prognosen erstellen, indem Sie multivariate Datenpunkte verwenden, d. h. Datenpunkte, die verschiedene Faktoren während desselben Zeitintervalls messen.
Sie könnten beispielsweise an den Verkäufen und Rückgaben eines Geschäfts während der Weihnachtszeit interessiert sein. Die folgende Tabelle zeigt einige Einträge aus zwei Datensätzen. Ein Datensatz zeigt die Verkäufe pro Tag und der andere Datensatz die Rückgaben pro Tag.
Tabelle 2. Beispielhafte Verkaufs- und Retourendaten Datum Umsatz (USD) Erträge (USD) 7. Dezember 2023 $63,788 $14,788 8. Dezember 2023 $105,678 $25,678 ... ... ... Obwohl die Tabelle Beobachtungen von zwei Tagen zeigt, muss Ihre Anfrage je nach dem von Ihnen verwendeten Modell zwischen 512 und 1.536 Datenpunkte enthalten. Wenn die Daten aus der Tabelle an die API übermittelt werden, werden sie wie folgt formatiert:
... "data": { "date": [ "2024-12-07T00:00:00", "2024-12-07T00:00:00", "2024-12-08T00:00:00", "2024-12-08T00:00:00", ... ], "ids": [ "sales", "returns", "sales", "returns", ... ], "sales": [ 63788, 105678, ... ], "returns": [ 14788, 25678 ... ] }
Beispiel
In diesem Beispiel wird das Modell granite-ttm-1536-96-r2 verwendet, um die Energieerzeugung für verschiedene Arten von Energiequellen pro Stunde vom 1. Januar 2015 bis zum 5. März 2015 vorherzusagen. Der im Beispiel verwendete Datensatz verfolgt die Energieerzeugung für ein europäisches Land und stammt von der Website des Europäischen Netzes der Übertragungsnetzbetreiber (ENTSO-E ).
Der Text der Anfrage ist in zwei Abschnitte unterteilt:
- Schema: Gibt an, welche Datensätze einbezogen werden und welche Felder analysiert werden sollen.
- Daten: Die zu analysierenden Daten.
Der Abschnitt schema sieht wie folgt aus:
"schema": {
"timestamp_column": "date",
"freq":"1h",
"target_columns": [
"gas",
"nuclear",
"solar"
]
},
Es werden nur Daten aus einem Datensatz übermittelt, sodass das optionale id_columns -Objekt aus dem Schema entfernt werden kann. Wenn mehr als ein Datensatz verwendet wird, muss das id_columns -Objekt enthalten sein, um die Quelle jedes Datenpunkts zu identifizieren.
Der Abschnitt data enthält Informationen über die Energieerzeugung pro Stunde aus verschiedenen Energiequellen, wie in der folgenden Tabelle dargestellt.
| Datum und Uhrzeit (Datum) | Erdgasenergie (Gas) | Kernenergie (nuklear) | Solarenergie (Solarenergie) |
|---|---|---|---|
| 2015-01-01T00:00:00 | 4844 | 7096 | 49 |
| 2015-01-01T01:00:00 | 5196 | 7096 | 50 |
| 2015-01-01T02:00:00 | 4857 | 7099 | 50 |
granite-ttm-1536-96-r2 benötigt mindestens 1.536 Datenpunkte pro Datensatz, um sicherzustellen, dass genügend Daten vorhanden sind, damit das Modell zukünftige Werte vorhersagen kann. Obwohl die Tabelle 3 Datenpunkte anzeigt, enthält der Anfragetext 1.536 Datenpunkte für jede Energiequelle.
Ersetzen Sie im folgenden Beispiel die Variable {url} durch den richtigen Wert für Ihre Instanz, z. B. us-south.ml.cloud.ibm.com. Fügen Sie Ihren eigenen Träger-Token und Ihre Projekt-ID hinzu.
Beispiel einer REST-Anfrage
curl -X POST \
'https://{url}/ml/v1/time_series/forecast?version=2025-01-10' \
--header 'Accept: application/json' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer eyJraWQiOi...' \
--data '{
...
}'
Die Daten-Nutzlast enthält das folgende JSON-Snippet:
{
"model_id":"ibm/granite-ttm-1536-96-r2",
"project_id":"4ab1437e-4c9d-47cf-9926-9ab52db1ee01",
"schema": {
"timestamp_column": "date",
"freq":"1h",
"target_columns": [
"gas",
"nuclear",
"solar"
]
},
"data": {
"date": [
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}
}
Beispiel einer REST-Antwort
Die folgende Antwort wird für die Beispielanfrage zurückgegeben.
{
"model_id": "ibm/granite-ttm-1536-96-r2",
"created_at": "2025-01-10T19:14:43.971Z",
"results": [
{
"date": [
"2015-03-06T00:00:00",
"2015-03-06T01:00:00",
"2015-03-06T02:00:00",
"2015-03-06T03:00:00",
"2015-03-06T04:00:00",
"2015-03-06T05:00:00",
"2015-03-06T06:00:00",
"2015-03-06T07:00:00",
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Ergebnisse
Das Zeitreihenmodell prognostizierte die zukünftigen Werte der Energieerzeugung für jede der drei Energiequellen für die nächsten 96 Stunden.
Das Modell ist in der Regel in der Lage, den allgemeinen Trend der Werte, einschließlich der Produktionsspitzen, korrekt vorherzusagen. Die prognostizierten Zahlen des Modells sind jedoch tendenziell konservativer als die tatsächlichen Zahlen.
Die Grafik im folgenden Screenshot zeigt die prognostizierte Erdgas-Energieerzeugung im Vergleich zur tatsächlichen Produktion im selben Zeitraum.
Die Grafik im folgenden Screenshot zeigt die prognostizierte Solarenergieerzeugung im Vergleich zur tatsächlichen Erzeugung im selben Zeitraum.
Weitere Informationen
Übergeordnetes Thema: Kodierung generativer KI-Lösungen