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Utilice los modelos de series temporales de la fundación IBM Granite y la API de previsión para pronosticar valores futuros
Utilice los modelos de series temporales de la fundación IBM Granite y la API de previsión para pronosticar valores futuros
Última actualización: 20 mar 2025
Utilice la API de previsión de series temporales y los modelos de series temporales IBM Granite disponibles en watsonx.ai para prever valores futuros a partir de datos históricos.
Formas de desarrollarse
Puede generar previsiones a partir de modelos de series temporales utilizando estos métodos de programación:
Visión general
Utilice la API de previsión de series temporales para pasar observaciones de datos históricos a un modelo de base de series temporales que pueda pronosticar valores futuros con inferencia de tiro cero. Por ejemplo, puede utilizar este método para pronosticar valores futuros a partir de observaciones temporales de los siguientes tipos de datos:
- Cotizaciones bursátiles y volúmenes de operaciones
- Datos de electrocardiograma (ECG) o registros de polisomnograma (PSG)
- Datos de temperatura o sismográficos
- Mediciones de rendimiento de red
Modelos de cimientos compatibles
Los modelos básicos de series temporales IBM Granite con los siguientes ID de modelo están disponibles para su uso en previsiones en watsonx.ai:
ibm/granite-ttm-512-96-r2: Requiere al menos 512 puntos de datos por canal en la solicitud.ibm/granite-ttm-1024-96-r2: Requiere al menos 1024 puntos de datos por canal en la solicitud.ibm/granite-ttm-1536-96-r2: Requiere al menos 1536 puntos de datos por canal en la solicitud.
Granite Los modelos de series temporales e IBM s son modelos compactos preentrenados para la previsión de series temporales multivariantes de Research, también conocidos como Tiny Time Mixers (TTM).
Los modelos de series temporales e Granite es funcionan mejor con puntos de datos en intervalos de minutos u horas y generan un conjunto de datos de previsión con 96 puntos de datos por canal de forma predeterminada. Para obtener los mejores resultados, utilice el modelo que tome el mayor número de puntos de datos en función de los datos de que disponga.
Para más información, consulte la ficha del modelo.
Para obtener mediante programación una lista de los modelos de series temporales disponibles para su uso, puede utilizar el método List the available foundation models de la API watsonx.ai as a service. Especifique el parámetro " filters=function_time_series_forecast " para obtener sólo los modelos de series temporales disponibles.
Por ejemplo:
curl -X GET \
'{url}/ml/v1/foundation_model_specs?version=2024-11-14&filters=function_time_series_forecast'
En el ejemplo, sustituya la variable " {url} " por el valor correcto para su instancia, como " us-south.ml.cloud.ibm.com.
Python
Puede escribir código que utilice los modelos de series temporales para pronosticar datos utilizando la biblioteca watsonx.ai Python. Para obtener más información, consulte las siguientes referencias:
- Interfaz de inferencia de modelos de series temporales
- Obtener el método de especificaciones del modelo de series temporales
Para obtener más información, consulte Uso de la API de previsión de series temporales de la Agencia Internacional de la Energía ( watsonx.ai ) para predecir la demanda de energía.
Ejemplo de cuaderno de notas de Python
Node.js
Puede escribir código que utilice los modelos de series temporales para pronosticar datos utilizando el SDK de watsonx.ai Node.js. Para obtener más información, consulte los siguientes recursos:
Para obtener más información, consulte el ejemplo de código.
API REST
Utilice el método de previsión de series temporales de la API REST de la Encuesta de la Comunidad de Tiffany ( watsonx.ai ) para pronosticar valores futuros basados en datos históricos.
Antes de empezar, revise los requisitos de datos y obtenga más información a partir de ejemplos de solicitudes.
- Requisitos de datos
- Información sobre facturación
- Ejemplo de solicitud
- Ejemplo de respuesta
- Resultados de ejemplo
Referencia de API
Para más detalles, consulte la documentación de referencia de la API.
Requisitos de datos de REST API
Una serie temporal es una colección de puntos de datos que se recogen a lo largo del tiempo.
Usted utiliza el siguiente esquema para describir los datos que incluye en la carga útil de la solicitud de la API:
"schema": {
"timestamp_column": "{date-column-name}",
"id_columns": [
"{series-name}",
"{series-name}"
]
"freq":"{time-interval}",
"target_columns": [
"{channel-name}",
"{channel-name}",
"{channel-name}"
]
},
Para utilizar los modelos de series temporales Granite para predecir valores futuros, los datos que envíe para su análisis deben cumplir los siguientes requisitos:
Los datos registrados deben ser numéricos, como temperaturas o cotizaciones bursátiles.
Sus datos deben incluir suficientes puntos de contexto histórico para que el modelo pueda hacer una predicción. Cada modelo de base de series temporales especifica un número mínimo requerido de puntos de datos por canal en la solicitud. Si especifica más del número requerido, el modelo utiliza los puntos de datos más recientes hasta el requisito del modelo e ignora el resto.
Debe haber un número igual de elementos en las matrices que se especifican para los
date,id_columnsytarget_columnsque se incluyen en el cuerpo de la solicitud. No puede omitir un punto de datos ni especificar "null" como punto de datos. Compruebe sus datos antes de enviar la solicitud.Aunque el servicio acepta los formatos de fecha y hora más comunes, cuando especifique fechas con marcas de tiempo en formato ISO 8601 (
2024-11-12T15:06:35), evitará la confusión que puede surgir debido a las diferencias en las convenciones de formato de fecha. Por ejemplo, ¿11/12/2024 significa 12 de noviembre u 11 de diciembre? Especifique las fechas en formato ISO 8601 con la diferencia horaria del tiempo universal coordinado (UTC) (2024-11-12T15:06:35+0000) para evitar duplicados aparentes o marcas de tiempo faltantes en los resultados de previsión generados.Muestrear los datos con una frecuencia uniforme. Por ejemplo, los datos pueden observarse en incrementos de 1 minuto, 1 hora o 1 día. No se genera ningún error si las marcas de tiempo no son uniformes, pero la calidad de los resultados puede ser deficiente. Los datos de previsión generados también se formatean con la frecuencia que se especifica con el parámetro "
freq".Por ejemplo, si especifica una frecuencia de un día (
"freq":"1D"), sus marcas de tiempo probablemente tengan este aspecto:"date": [ "2024-11-15T15:06:35", "2024-11-16T15:06:35", ... ]Si la frecuencia es de 5 minutos (
"freq":"5min"), es probable que cada marca de fecha y hora esté separada por 5 minutos."date": [ "2024-11-15T15:06:35", "2024-11-15T15:11:35", ... ]Para conocer los valores admitidos para las abreviaturas de fecha y hora en el parámetro de frecuencia, consulte Alias de periodo en la documentación de la biblioteca pandas.
Si incluye puntos de datos de más de una fuente para el análisis, cada punto de datos debe proporcionar un identificador único para indicar a qué serie, o conjunto de observaciones, pertenece.
Por ejemplo, si está analizando las tendencias de compra, puede que le interesen las cifras de ventas de varias tiendas durante la temporada navideña. La siguiente tabla muestra dos series, A y B. La serie A muestra las ventas diarias de la ubicación A y la serie B muestra las ventas diarias de la ubicación B.
Tabla 1. Muestra de datos de ventas Fecha Identificación de la serie Ventas totales (USD) 7 de diciembre de 2023 A $24,988 7 de diciembre de 2023 B $63,788 8 de diciembre de 2023 A $41,855 8 de diciembre de 2023 B $105,678 ... ... ... Aunque la tabla muestra observaciones de dos días, su solicitud debe incluir entre 512 y 1.536 puntos de datos, dependiendo del modelo que utilice. Cuando los datos de la tabla se envían a la API, tienen el siguiente formato:
... "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, ... ] }Puede realizar previsiones utilizando puntos de datos multivariantes, es decir, puntos de datos que miden distintos factores durante el mismo intervalo de tiempo.
Por ejemplo, puede interesarle tanto las ventas como las devoluciones de una tienda durante la temporada navideña. La siguiente tabla muestra algunas entradas de dos canales, o diferentes dimensiones o aspectos de los datos registrados. Un canal muestra las ventas por día y el otro canal muestra las devoluciones por día.
Tabla 2. Muestra de datos de ventas y devoluciones Fecha Ventas (USD) Rentabilidad (USD) 7 de diciembre de 2023 $63,788 $14,788 8 de diciembre de 2023 $105,678 $25,678 ... ... ... Aunque la tabla muestra observaciones de dos días, su solicitud debe incluir entre 512 y 1.536 puntos de datos, dependiendo del modelo que utilice. El campo id_column se omite porque los datos proceden de la misma serie, una recopilación de observaciones de una sola tienda. Cuando los datos de la tabla se envían a la API, tienen el siguiente formato:
... "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 ... ] }Puede realizar previsiones utilizando puntos de datos multivariantes de más de una serie de observaciones. Por ejemplo, puede combinar eficazmente los dos escenarios anteriores para pronosticar las ventas y devoluciones tanto de la tienda A como de la tienda B.
Los datos de la solicitud pueden tener un aspecto similar a este:
"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, ... ]
Información sobre facturación
Los cargos por la previsión del modelo de cimientos se calculan utilizando las siguientes ecuaciones:
- Cálculo de entrada:
context length x number of series x number of channels - Cálculo de la salida:
prediction length x number of series x number of channels
Estas ecuaciones utilizan los siguientes valores:
La longitud del contexto se refiere al número de puntos de datos históricos que un modelo de base de series temporales utiliza como entrada para hacer predicciones.
Por ejemplo, cuando la longitud del contexto es de
1536, el modelo utiliza los últimos 1536 puntos de datos, como los precios de las acciones en los últimos 1536 días, para hacer predicciones. La longitud del contexto está determinada por el modelo de base que se utiliza para la previsión.Una serie es una colección de observaciones realizadas de forma secuencial a lo largo del tiempo.
Por ejemplo, al comparar los precios de las acciones de muchas empresas, el historial de precios de las acciones observado para cada empresa es una serie independiente. El conjunto de series que se va a medir se especifica en el segmento "
ID_column" del cuerpo de la solicitud API.Los canales son características o variables específicas dentro de un conjunto de datos de series temporales. En los datos de series temporales multivariantes, cada canal representa una dimensión o aspecto diferente de los datos registrados.
Por ejemplo, en un conjunto de datos de precios de acciones, los canales pueden incluir el precio de apertura, el precio de cierre, el volumen y las medias móviles. Cada variable medida representa un canal independiente. Los canales que se van a medir se especifican en el segmento "
target_columns" del cuerpo de la solicitud de la API.La longitud de predicción es el número de puntos de datos futuros que el modelo debe predecir. Cuando la longitud de predicción solicitada es de
96, el modelo pronostica los siguientes 96 puntos de datos.Por ejemplo, el modelo predice los precios de las acciones para los próximos 96 días. La duración de la predicción puede especificarse incluyendo el parámetro
prediction_lengthen la solicitud de la API.
Para obtener más información sobre el precio de los puntos de datos de entrada y salida y cómo calcular los costes, consulte Detalles de facturación para activos de IA generativa.
Ejemplo de API REST
Este ejemplo utiliza el modelo e granite-ttm-1536-96-r2, para pronosticar la demanda total de energía del país de Portugal por hora desde el 30 de septiembre de 2024 hasta el 2 de diciembre de 2024. El conjunto de datos utilizado en el ejemplo procede del sitio web de la Red Europea de Gestores de Redes de Transporte de Electricidad (ENTSO-E ). Para ver los datos, vaya a esta página del sitio de ENTSO-E, seleccione Portugal como país y, a continuación, elija el 29 de septiembre de 2024 como fecha con la zona horaria predeterminada (CET).
La solicitud de la API no especifica un parámetro de prediction_length. Por lo tanto, se utiliza la duración predeterminada para el modelo de base, lo que significa que el modelo de series temporales predecirá los valores futuros de la demanda de energía durante las próximas 96 horas.
El cuerpo de la solicitud se divide en dos secciones:
- Esquema: Especifica qué conjuntos de datos se incluirán y qué campos analizar.
- Los datos: Los datos que se van a analizar.
La sección schema tiene este aspecto:
"schema": {
"freq": "1h",
"timestamp_column": "Time (CET/CEST)",
"target_columns": [
"Actual Total Load [MW]"
]
},
Puede que observe que no hay ningún objeto " id_columns " incluido en el esquema. En este ejemplo, se envían datos de una sola serie de datos observados, lo que significa que el objeto " id_columns " puede omitirse en el esquema. Sin embargo, si su solicitud incluye datos de más de una fuente, debe incluir el objeto id_columns para identificar la fuente de cada punto de datos.
La sección data incluye información sobre la demanda de energía por hora del país Portugal, tal como se muestra en la siguiente tabla.
| Fecha y hora (fecha) | Demanda total de energía (MW) |
|---|---|
| 2024-09-30T00:00:00 | 4941 |
| 2024-09-30T01:00:00 | 4640 |
| 2024-09-30T02:00:00 | 4443 |
El modelo e granite-ttm-1536-96-r2 a requiere al menos 1536 puntos de datos por canal en la solicitud para garantizar que haya suficientes datos para que el modelo prediga valores futuros. Aunque la tabla muestra 3 puntos de datos, el cuerpo de la solicitud contiene 1536 puntos de datos.
En el siguiente ejemplo, sustituya la variable {url} por el valor correcto para su instancia, como us-south.ml.cloud.ibm.com. Añade tu propio token al portador y tu ID de proyecto.
Ejemplo de solicitud de REST API
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 '{
...
}'
La carga útil de datos contiene el siguiente fragmento JSON:
{
"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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}
}
Ejemplo de respuesta REST API
Se devuelve la siguiente respuesta para la solicitud de ejemplo.
{
"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,
6571.35986328125,
6776.26318359375,
6817.2890625,
6812.52001953125,
6746.37744140625,
6707.9755859375,
6773.8466796875,
6847.83154296875,
6995.001953125,
7263.904296875,
7553.798828125,
7598.19580078125,
7379.0302734375,
6916.89697265625,
6339.57373046875,
5786.80908203125,
5321.87109375,
5020.8017578125,
4870.2451171875,
4778.8359375,
4778.078125,
4953.40869140625,
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"Time (CET/CEST)": [
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"2024-12-05T09:00:00",
"2024-12-05T10:00:00",
"2024-12-05T11:00:00",
"2024-12-05T12:00:00",
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"2024-12-06T22:00:00"
]
}
],
"input_data_points": 1536,
"output_data_points": 96
}
Resultados de ejemplo de REST API
El modelo de series temporales predijo los valores futuros de la demanda de energía en Portugal durante las siguientes 96 horas.
El gráfico de la siguiente captura de pantalla muestra la demanda de energía prevista en comparación con la demanda de energía real durante ese mismo período de tiempo.
Más información
Tema principal: Codificación de soluciones de IA generativa
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