Utilisez les modèles de base de séries temporelles et l'API de prévision de IBM Granite pour prévoir les valeurs futures
Dernière mise à jour : 20 mars 2025
Utilisez l'API de prévision des séries temporelles et les modèles de séries temporelles IBM Granite disponibles sur watsonx.ai pour prévoir les valeurs futures sur la base des données historiques.
Moyens de développement
Ces méthodes de programmation permettent de générer des prévisions à partir de modèles de fondations de séries temporelles :
Présentation
Utilisez l'API de prévision de séries chronologiques pour transmettre des observations de données historiques à un modèle de base de séries chronologiques capable de prévoir des valeurs futures avec une inférence à zéro coup. Par exemple, vous pouvez utiliser cette méthode pour prévoir des valeurs futures à partir d'observations temporelles des types de données suivants :
- Cours d'action et volumes d'échange
- Données de l'électrocardiogramme (EKG) ou enregistrements du polysomnogramme (PSG)
- Données de température ou sismographiques
- Mesures des performances réseau
Modèles de fondations soutenus
Les modèles de base de séries temporelles IBM Granite avec les identifiants de modèle suivants sont disponibles pour les prévisions dans watsonx.ai:
ibm/granite-ttm-512-96-r2ibm/granite-ttm-1024-96-r2ibm/granite-ttm-1536-96-r2
Les modèles de séries temporelles Granite sont des modèles compacts pré-entraînés pour les prévisions de séries temporelles multivariées de IBM Research, également connus sous le nom de Tiny Time Mixers (TTM).
Les modèles de séries temporelles de Granite fonctionnent mieux avec des points de données à intervalles d'une minute ou d'une heure et génèrent par défaut un ensemble de données de prévision avec 96 points de données par canal. Pour obtenir les meilleurs résultats, utilisez le modèle qui prend le plus grand nombre de points de données en fonction des données dont vous disposez.
Pour plus d'informations, voir la carte du modèle.
Pour obtenir de manière programmatique une liste des modèles de séries temporelles disponibles, vous pouvez utiliser la méthode List the available foundation models dans l'API watsonx.ai as a service. Spécifiez le paramètre " filters=function_time_series_forecast
Par exemple :
curl -X GET \ '{url}/ml/v1/foundation_model_specs?version=2024-11-14&filters=function_time_series_forecast'
Dans l'exemple, remplacez la variable " {url}us-south.ml.cloud.ibm.com
Python
Vous pouvez écrire du code qui utilise les modèles de séries temporelles pour prévoir les données en utilisant la bibliothèque Python watsonx.ai Pour en savoir davantage, reportez-vous aux références suivantes :
- Interface d'inférence de modèles de séries temporelles
- Obtenir la méthode d'analyse des modèles de séries temporelles
Pour en savoir plus, voir Utilisation de l'API de prévision des séries temporelles watsonx.ai pour prévoir la demande d'énergie.
Exemple de carnet Python
Node.js
Vous pouvez écrire du code qui utilise les modèles de séries chronologiques pour prévoir des données en utilisant le kit de développement logiciel (SDK) watsonx.ai Node.js. Pour plus d'informations, voir les ressources suivantes :
Pour en savoir plus, consultez l'exemple de code.
API REST
Utilisez la méthode de prévision des séries temporelles de l'API REST watsonx.ai pour prévoir les valeurs futures sur la base des données historiques.
Avant de commencer, passez en revue les exigences en matière de données et apprenez-en plus sur les demandes d'échantillons.
- Exigences en données
- Informations sur la facturation
- Exemple de demande
- Exemple de réponse
- Exemple de résultats
Référence d'interface de programme d'application
Pour plus de détails, voir la documentation de référence de l'API.
Exigences relatives aux données de l'API REST
Une série chronologique est une collection de points de données collectés au fil du temps.
Vous utilisez le schéma suivant pour décrire les données que vous incluez dans les données utiles de la demande d'API :
"schema": { "timestamp_column": "{date-column-name}", "id_columns": [ "{series-name}", "{series-name}" ] "freq":"{time-interval}", "target_columns": [ "{channel-name}", "{channel-name}", "{channel-name}" ] },
Pour utiliser les modèles de séries temporelles de Granite afin de prévoir les valeurs futures, les données que vous soumettez à l'analyse doivent répondre aux exigences suivantes :
Les données enregistrées doivent être numériques, comme les températures ou les cours des actions.
Vos données doivent inclure suffisamment de points de données du contexte historique pour que le modèle puisse faire une prédiction. Chaque modèle de base de série chronologique spécifie un nombre minimum requis de points de données par canal dans la requête. Si vous spécifiez plus que le nombre requis, le modèle utilise les points de données les plus récents jusqu'à l'exigence du modèle et ignore le reste.
Il doit y avoir un nombre égal d'éléments dans les tableaux spécifiés pour les
dateid_columnstarget_columnsnullBien que le service accepte la plupart des formats de date et d'heure courants, lorsque vous spécifiez des dates et des horodatages au format ISO 8601 (
2024-11-12T15:06:352024-11-12T15:06:35+0000Échantillonner les données à une fréquence uniforme. Par exemple, les données peuvent être observées par incréments d'une minute, d'une heure ou d'un jour. Aucune erreur n'est générée si vos horodatages ne sont pas uniformes, mais la qualité de vos résultats risque d'être médiocre. Les données prévisionnelles générées sont également formatées avec la fréquence que vous spécifiez avec le paramètre "
freqPar exemple, si vous spécifiez une fréquence d'un jour (
"freq":"1D""date": [ "2024-11-15T15:06:35", "2024-11-16T15:06:35", ... ]Si la fréquence est de 5 minutes (
"freq":"5min""date": [ "2024-11-15T15:06:35", "2024-11-15T15:11:35", ... ]Pour connaître les valeurs prises en charge pour les abréviations de date et d'heure dans le paramètre frequency, voir Period aliases dans la documentation de la bibliothèque pandas.
Si vous incluez des points de données provenant de plusieurs sources pour l'analyse, chaque point de données doit fournir un identifiant unique pour indiquer à quelle série, ou collection d'observations, il appartient.
Par exemple, si vous analysez les tendances d'achat, vous pourriez être intéressé par les chiffres de vente de plusieurs magasins pendant les fêtes de fin d'année. Le tableau suivant présente deux séries, A et B. La série A indique les ventes par jour pour le site A et la série B indique les ventes par jour pour le site B.
Tableau 1. Exemple de données sur les ventes Date : ID de la série Total des ventes (USD) 7 décembre 2023 A $24,988 7 décembre 2023 B $63,788 8 décembre 2023 A $41,855 8 décembre 2023 B $105,678 ... ... ... Bien que le tableau présente des observations sur deux jours, votre demande doit inclure entre 512 et 1 536 points de données, en fonction du modèle que vous utilisez. Lorsque les données du tableau sont transmises à l'API, elles sont formatées comme suit :
... "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, ... ] }Vous pouvez faire des prévisions en utilisant des points de données multivariés, c'est-à-dire des points de données qui mesurent différents facteurs au cours du même intervalle de temps.
Par exemple, vous pouvez être intéressé par les ventes et les retours d'un magasin pendant la période des fêtes. Le tableau suivant montre quelques entrées provenant de deux canaux, ou de dimensions ou d'aspects différents des données enregistrées. Un canal indique les ventes par jour et l'autre canal indique les retours par jour.
Tableau 2. Échantillon de données sur les ventes et les retours Date : Ventes (USD) Rendement (USD) 7 décembre 2023 $63,788 $14,788 8 décembre 2023 $105,678 $25,678 ... ... ... Bien que le tableau présente des observations sur deux jours, votre demande doit inclure entre 512 et 1 536 points de données, en fonction du modèle que vous utilisez. Le champ id_column est omis car les données proviennent de la même série, une collection d'observations provenant d'un seul magasin. Lorsque les données du tableau sont transmises à l'API, elles sont formatées comme suit :
... "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 ... ] }Vous pouvez faire des prévisions en utilisant des points de données multivariées provenant de plus d'une série d'observations. Par exemple, vous pouvez combiner efficacement les deux scénarios précédents pour prévoir les ventes et les retours pour le magasin A et le magasin B.
Les données contenues dans la demande peuvent ressembler à ceci :
"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, ... ]
Informations sur la facturation
Les charges pour la prévision du modèle de fondation sont calculées à l'aide des équations suivantes :
- Calcul des entrées :
context length x number of series x number of channels - Calcul de la production :
prediction length x number of series x number of channels
Ces équations utilisent les valeurs suivantes :
La longueur du contexte fait référence au nombre de points de données historiques qu'un modèle de base de série chronologique utilise comme entrée pour faire des prédictions.
Par exemple, lorsque la longueur du contexte est
1536Une série est un ensemble d'observations effectuées séquentiellement dans le temps.
Par exemple, lorsque l'on compare les prix des actions de plusieurs entreprises, l'historique des prix des actions observés pour chaque entreprise constitue une série distincte. L'ensemble des séries à mesurer est spécifié dans le segment
ID_columnLes canaux sont des caractéristiques ou des variables spécifiques au sein d'un ensemble de données de séries temporelles. Dans les séries chronologiques multivariées, chaque canal représente une dimension ou un aspect différent des données enregistrées.
Par exemple, dans un ensemble de données sur le cours des actions, les canaux peuvent inclure le cours d'ouverture, le cours de clôture, le volume et les moyennes mobiles. Chaque variable mesurée représente un canal distinct. Les canaux à mesurer sont spécifiés dans le segment
target_columnsLa longueur de la prédiction est le nombre de points de données futurs que le modèle doit prédire. Lorsque la longueur de prédiction demandée est
96Par exemple, le modèle prédit les prix des actions pour les 96 jours à venir. La longueur de la prédiction peut être spécifiée en incluant le paramètre
prediction_length
Pour plus d'informations sur la tarification des points de données d'entrée et de sortie et sur la manière de calculer les coûts, voir Détails de la facturation pour les actifs d'IA générative.
Exemple d'API REST
Cet exemple utilise le modèle granite-ttm-1536-96-r2
La demande d'API ne spécifie pas de paramètre prediction_length
Le corps de la demande est divisé en deux sections :
- Schéma : Spécifie les ensembles de données à inclure et les champs à analyser.
- Données : Les données à analyser.
La section schema
"schema": { "freq": "1h", "timestamp_column": "Time (CET/CEST)", "target_columns": [ "Actual Total Load [MW]" ] },
Vous remarquerez peut-être qu'il n'y a pas d'objet id_columnsid_columnsid_columns
La section data
| Date et heure (date) | Demande totale d'énergie (MW) |
|---|---|
| 2024-09-30T00:00:00 | 4941 |
| 2024-09-30T01:00:00 | 4640 |
| 2024-09-30T02:00:00 | 4443 |
Le modèle granite-ttm-1536-96-r2 nécessite au moins 1 536 points de données par canal dans la requête afin de garantir qu'il y a suffisamment de données pour que le modèle puisse prédire les valeurs futures. Bien que le tableau indique 3 points de données, le corps de la requête contient 1 536 points de données.
Dans l'exemple suivant, remplacez la variable {url}us-south.ml.cloud.ibm.com
Exemple de demande d'API REST
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 '{ ... }'
Les données utiles contiennent l'extrait JSON suivant :
{
"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",
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}
}
Exemple de réponse à l'API REST
La réponse suivante est renvoyée pour l'exemple de demande.
{
"model_id": "ibm/granite-ttm-1536-96-r2",
"created_at": "2025-02-05T14:00:53.597Z",
"results": [
{
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"Time (CET/CEST)": [
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]
}
],
"input_data_points": 1536,
"output_data_points": 96
}
Résultats de l'exemple de l'API REST
Le modèle de série temporelle a prédit les valeurs futures de la demande d'énergie au Portugal au cours des 96 heures suivantes.
Le graphique de la capture d'écran suivante montre la demande d'énergie prévue par rapport à la demande d'énergie réelle au cours de la même période.
En savoir plus
Sujet parent : Coder des solutions d'IA générative
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