Verwenden Sie die Zeitreihen-Grundmodelle IBM Granite und die Prognose-API, um zukünftige Werte vorherzusagen
Letzte Aktualisierung: 20. März 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.
Entwicklungsmöglichkeiten
Sie können Prognosen aus Zeitreihen-Grundmodellen mithilfe dieser Programmiermethoden erstellen:
Übersicht
Verwenden Sie die Zeitreihenprognose-API, um historische Datenbeobachtungen an ein Zeitreihen-Grundmodell zu übergeben, das zukünftige Werte mit Null-Schuss-Inferenz vorhersagen kann. Sie können diese Methode beispielsweise verwenden, um zukünftige Werte aus zeitbasierten Beobachtungen der folgenden Datentypen vorherzusagen:
- 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 Kanal in der Anfrage.ibm/granite-ttm-1024-96-r2: Erfordert mindestens 1.024 Datenpunkte pro Kanal in der Anfrage.ibm/granite-ttm-1536-96-r2: Erfordert mindestens 1.536 Datenpunkte pro Kanal in der Anfrage.
Die Granite -Zeitreihenmodelle sind kompakte vortrainierte Modelle für multivariate Zeitreihenprognosen von IBM Research, auch bekannt als Tiny Time Mixers (TTM).
Die Zeitreihenmodelle von Granite funktionieren am besten mit Datenpunkten in Minuten- oder Stundenintervallen und generieren standardmäßig einen Prognosedatensatz mit 96 Datenpunkten pro Kanal. 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.
Python
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:
- Schnittstelle für die Inferenz von Zeitreihenmodellen
- Abrufen von Zeitreihenmodellspezifikationen Methode
Weitere Informationen finden Sie unter "Verwendung der watsonx.ai Time Series Forecasting API zur Vorhersage des Energiebedarfs ".
Beispiel für ein Python -Notizbuch
Verwendung von Time Series Foundation-Modellen und Zeitreihendaten zur Vorhersage des Energiebedarfs
Node.js
Sie können Code schreiben, der die Zeitreihenmodelle zur Vorhersage von Daten verwendet, indem Sie das SDK watsonx.ai Node.js verwenden. Weitere Informationen können Sie über die folgenden Links aufrufen:
Weitere Informationen finden Sie im Codebeispiel.
REST-API
Verwenden Sie die Zeitreihenprognose-Methode der watsonx.ai REST API, um zukünftige Werte auf der Grundlage historischer Daten vorherzusagen.
Bevor Sie beginnen, sollten Sie sich über die Datenanforderungen informieren und anhand von Musteranfragen mehr erfahren.
API-Referenz
Einzelheiten finden Sie in der API-Referenzdokumentation.
REST-API-Datenanforderungen
Eine Zeitreihe ist eine Sammlung von Datenpunkten, die im Laufe der Zeit erhoben werden.
Verwenden Sie das folgende Schema, um die Daten zu beschreiben, die Sie in die Nutzlast der API-Anfrage aufnehmen:
"schema": {
"timestamp_column": "{date-column-name}",
"id_columns": [
"{series-name}",
"{series-name}"
]
"freq":"{time-interval}",
"target_columns": [
"{channel-name}",
"{channel-name}",
"{channel-name}"
]
},
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. Jedes Zeitreihen-Grundmodell gibt in der Anfrage eine erforderliche Mindestanzahl von Datenpunkten pro Kanal an. Wenn Sie mehr als die erforderliche Anzahl angeben, verwendet das Modell die jüngsten Datenpunkte bis zur Modellanforderung und ignoriert den Rest.
Die Anzahl der Elemente in den Arrays, die für
date,id_columnsundtarget_columnsangegeben sind und im Textkörper der Anfrage enthalten sind, muss gleich sein. 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 Datumsformatierungskonventionen entstehen kann, wenn Sie Datumsangaben 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 ISO 8601-Format mit dem UTC-Offset (koordinierte Weltzeit) an (2024-11-12T15:06:35+0000), um offensichtliche Duplikate oder fehlende Zeitstempel in den generierten Prognoseergebnissen 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 Frequenzparameter finden Sie unter Perioden-Aliasnamen in der Dokumentation für die Pandas-Bibliothek.
Wenn Sie Datenpunkte aus mehr als einer Quelle für die Analyse verwenden, muss jeder Datenpunkt eine eindeutige Kennung enthalten, die angibt, zu welcher Serie oder Sammlung von Beobachtungen 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 Reihen, A und B. Reihe A zeigt die Verkäufe pro Tag für Standort A und Reihe B zeigt die Verkäufe pro Tag für Standort B.
Tabelle 1. Beispielhafte Verkaufsdaten Datum Seriennummer 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 Kanälen oder verschiedene Dimensionen oder Aspekte der aufgezeichneten Daten. Ein Kanal zeigt die Verkäufe pro Tag und der andere Kanal zeigt die Retouren 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. Das Feld "id_column" wird ausgelassen, da die Daten aus derselben Reihe stammen, einer Sammlung von Beobachtungen aus einem einzigen Geschäft. 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", ... ], "sales": [ 63788, 105678, ... ], "returns": [ 14788, 25678 ... ] }Sie können Prognosen erstellen, indem Sie multivariate Datenpunkte aus mehr als einer Beobachtungsreihe verwenden. Sie können beispielsweise die beiden vorherigen Szenarien effektiv kombinieren, um die Umsätze und Retouren für Geschäft A und Geschäft B zu prognostizieren.
Die Daten in der Anfrage könnten etwa so aussehen:
"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, // sales for store A on 7 December 2024 63788, // sales for store B on 7 December 2024 41855, 105678, ... ], "returns": [ 3997, // returns for store A on 7 December 2024 14788, // returns for store B on 7 December 2024 5768, 25678, ... ]
Abrechnungsinformationen
Die Gebühren für die Prognose des Gründungsmodells werden anhand der folgenden Gleichungen berechnet:
- Eingabeberechnung:
context length x number of series x number of channels - Output-Berechnung:
prediction length x number of series x number of channels
Diese Gleichungen verwenden die folgenden Werte:
Die Kontextlänge bezieht sich auf die Anzahl der historischen Datenpunkte, die ein Zeitreihen-Grundmodell als Eingabe für Vorhersagen verwendet.
Wenn die Kontextlänge beispielsweise
1536beträgt, verwendet das Modell die letzten 1536 Datenpunkte, wie z. B. die Aktienkurse der letzten 1536 Tage, um Vorhersagen zu treffen. Die Kontextlänge wird durch das für die Prognose verwendete Fundamentmodell bestimmt.Eine Reihe ist eine Sammlung von Beobachtungen, die nacheinander im Laufe der Zeit gemacht werden.
Wenn man beispielsweise die Aktienkurse vieler Unternehmen vergleicht, ist die beobachtete Aktienkursentwicklung für jedes Unternehmen eine separate Reihe. Die zu messende Reihe wird im
ID_column-Segment des API-Anfragetextes angegeben.Kanäle sind spezifische Merkmale oder Variablen innerhalb eines Zeitreihendatensatzes. In multivariaten Zeitreihendaten stellt jeder Kanal eine andere Dimension oder einen anderen Aspekt der aufgezeichneten Daten dar.
In einem Aktienkursdatensatz könnten Kanäle beispielsweise den Eröffnungskurs, den Schlusskurs, das Volumen und gleitende Durchschnitte umfassen. Jede gemessene Variable stellt einen separaten Kanal dar. Die zu messenden Kanäle werden im
target_columns-Segment des API-Anfragetextes angegeben.Die Vorhersagelänge ist die Anzahl der zukünftigen Datenpunkte, die das Modell vorhersagen soll. Wenn die angeforderte Vorhersagelänge
96beträgt, prognostiziert das Modell die nächsten 96 Datenpunkte.Das Modell sagt beispielsweise die Aktienkurse für die nächsten 96 Tage voraus. Die Länge der Vorhersage kann durch Einfügen des Parameters
prediction_lengthin die API-Anfrage angegeben werden.
Weitere Informationen zu den Preisen für Eingabe- und Ausgabedatenpunkte und zur Berechnung der Kosten finden Sie unter Abrechnungsdetails für generative KI-Ressourcen.
Beispiel für REST-API
In diesem Beispiel wird das Modell granite-ttm-1536-96-r2 verwendet, um den Gesamtenergiebedarf für das Land Portugal pro Stunde vom 30. September 2024 bis zum 2. Dezember 2024 vorherzusagen. Der im Beispiel verwendete Datensatz stammt von der Website des Europäischen Netzes der Übertragungsnetzbetreiber (ENTSO-E ). Um die Daten anzuzeigen, gehen Sie auf diese Seite auf der ENTSO-E-Website, wählen Sie Portugal als Land aus und wählen Sie dann den 29. September 2024 als Datum mit der Standardzeitzone (MEZ) aus.
Die API-Anfrage enthält keinen prediction_length -Parameter. Daher wird die Standardlänge für das Fundamentmodell verwendet, was bedeutet, dass das Zeitreihenmodell zukünftige Werte für den Energiebedarf in den nächsten 96 Stunden vorhersagen wird.
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": {
"freq": "1h",
"timestamp_column": "Time (CET/CEST)",
"target_columns": [
"Actual Total Load [MW]"
]
},
Sie werden vielleicht bemerken, dass im Schema kein id_columns -Objekt enthalten ist. In diesem Beispiel werden Daten aus nur einer Reihe beobachteter Daten übermittelt, was bedeutet, dass das id_columns -Objekt im Schema ausgelassen werden kann. Wenn Ihre Anfrage jedoch Daten aus mehr als einer Quelle enthält, müssen Sie das Objekt id_columns angeben, um die Quelle jedes Datenpunkts zu identifizieren.
Der Abschnitt data enthält Informationen über den Energiebedarf pro Stunde für das Land Portugal, wie in der folgenden Tabelle dargestellt.
| Datum und Uhrzeit (Datum) | Gesamtenergiebedarf (MW) |
|---|---|
| 2024-09-30T00:00:00 | 4941 |
| 2024-09-30T01:00:00 | 4640 |
| 2024-09-30T02:00:00 | 4443 |
Das granite-ttm-1536-96-r2 -Modell erfordert mindestens 1.536 Datenpunkte pro Kanal in der Anfrage, um sicherzustellen, dass genügend Daten für das Modell zur Vorhersage zukünftiger Werte vorhanden sind. Obwohl die Tabelle 3 Datenpunkte anzeigt, enthält der Anfragetext 1.536 Datenpunkte.
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 für eine REST-API-Anfrage
curl -X POST \
'https://{region}.ml.cloud.ibm.com/ml/v1/time_series/forecast?version=2025-02-10' \
--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": "51f3a990-4372-4ac3-9ddb-ed99d9b50840",
"schema": {
"freq": "1h",
"timestamp_column": "Time (CET/CEST)",
"target_columns": [
"Actual Total Load [MW]"
]
},
"data": {
"Time (CET/CEST)": [
"2024-09-30T00:00:00",
"2024-09-30T01:00:00",
"2024-09-30T02:00:00",
"2024-09-30T03:00:00",
"2024-09-30T04:00:00",
"2024-09-30T05:00:00",
"2024-09-30T06:00:00",
"2024-09-30T07:00:00",
"2024-09-30T08:00:00",
"2024-09-30T09:00:00",
"2024-09-30T10:00:00",
"2024-09-30T11:00:00",
"2024-09-30T12:00:00",
"2024-09-30T13:00:00",
"2024-09-30T14:00:00",
"2024-09-30T15:00:00",
"2024-09-30T16:00:00",
"2024-09-30T17:00:00",
"2024-09-30T18:00:00",
"2024-09-30T19:00:00",
"2024-09-30T20:00:00",
"2024-09-30T21:00:00",
"2024-09-30T22:00:00",
"2024-09-30T23:00:00",
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}
}
Beispiel für eine REST-API-Antwort
Die folgende Antwort wird für die Beispielanfrage zurückgegeben.
{
"model_id": "ibm/granite-ttm-1536-96-r2",
"created_at": "2025-02-05T14:00:53.597Z",
"results": [
{
"Actual Total Load [MW]": [
6465.83154296875,
5847.07080078125,
5351.2109375,
5033.18212890625,
4876.1064453125,
4780.388671875,
4784.177734375,
4964.26171875,
5416.59375,
6017.84619140625,
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"Time (CET/CEST)": [
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}
],
"input_data_points": 1536,
"output_data_points": 96
}
REST-API-Beispielergebnisse
Das Zeitreihenmodell prognostizierte zukünftige Werte für den Energiebedarf in Portugal in den nächsten 96 Stunden.
Die Grafik im folgenden Screenshot zeigt den prognostizierten Energiebedarf im Vergleich zum tatsächlichen Energiebedarf im selben Zeitraum.
Weitere Informationen
Übergeordnetes Thema: Kodierung generativer KI-Lösungen
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