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ARIMA_PLUS de BigQuery ML es un modelo de previsión de una variable. Como modelo estadístico, es más rápido para entrenar que un modelo basado en redes neuronales.
Recomendamos entrenar un modelo ARIMA_PLUS de BigQuery ML si necesitas realizar muchas iteraciones rápidas de entrenamiento de modelos o si necesitas un modelo de referencia económico para medir otros modelos.
Al igual que Prophet, ARIMA_PLUS de BigQuery ML intenta descomponer cada serie temporal en tendencias, temporadas y días feriados, y produce una previsión mediante la agregación de las predicciones de estos modelos. Sin embargo, una de las muchas diferencias es que ARIMA+ de BQML usa ARIMA para modelar el componente de tendencia, mientras que Prophet intenta ajustar una curva mediante un modelo logístico o lineal por partes.
Google Cloud ofrece una canalización para entrenar un modelo ARIMA_PLUS de BigQuery ML y una canalización a fin de obtener predicciones por lotes a partir de un modelo ARIMA_PLUS de BigQuery ML.
Ambas canalizaciones son instancias de Vertex AI Pipelines de componentes de canalización de Google Cloud (GCPC).
[[["Fácil de comprender","easyToUnderstand","thumb-up"],["Resolvió mi problema","solvedMyProblem","thumb-up"],["Otro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Información o código de muestra incorrectos","incorrectInformationOrSampleCode","thumb-down"],["Faltan la información o los ejemplos que necesito","missingTheInformationSamplesINeed","thumb-down"],["Problema de traducción","translationIssue","thumb-down"],["Otro","otherDown","thumb-down"]],["Última actualización: 2024-12-09 (UTC)"],[],[],null,["# Forecasting with ARIMA+\n\n| To see an example of how to train a model with ARIMA+,\n| run the \"Train a BigQuery ML ARIMA_PLUS model using Vertex AI tabular workflows\" notebook in one of the following\n| environments:\n|\n| [Open in Colab](https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabular_workflows/bqml_arima_plus.ipynb)\n|\n|\n| \\|\n|\n| [Open in Colab Enterprise](https://console.cloud.google.com/vertex-ai/colab/import/https%3A%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Ftabular_workflows%2Fbqml_arima_plus.ipynb)\n|\n|\n| \\|\n|\n| [Open\n| in Vertex AI Workbench](https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https%3A%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Ftabular_workflows%2Fbqml_arima_plus.ipynb)\n|\n|\n| \\|\n|\n| [View on GitHub](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabular_workflows/bqml_arima_plus.ipynb)\n\n\n[BigQuery ML ARIMA_PLUS](/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-time-series) is a univariate forecasting model. As\na statistical model, it is faster to train than a [model based on neural networks](/vertex-ai/docs/tabular-data/forecasting/overview).\nWe recommend training a BigQuery ML ARIMA_PLUS model if you need to\nperform many quick iterations of model training or if you need an inexpensive\nbaseline to measure other models against.\n\nLike [Prophet](/vertex-ai/docs/tabular-data/forecasting-prophet),\nBigQuery ML ARIMA_PLUS attempts to decompose each time series into\ntrends, seasons, and holidays, producing a forecast using the aggregation of\nthese models' inferences. One of the many differences, however, is that\nBQML ARIMA+ uses ARIMA to model the trend component, while Prophet attempts to\nfit a curve using a piecewise logistic or linear model.\n\nGoogle Cloud offers a pipeline for training a BigQuery ML ARIMA_PLUS\nmodel and a pipeline for getting batch inferences from a BigQuery ML ARIMA_PLUS model.\nBoth pipelines are instances of\n[Vertex AI Pipelines](/vertex-ai/docs/pipelines/introduction) from\n[Google Cloud Pipeline Components](/vertex-ai/docs/pipelines/components-introduction) (GCPC).\n\nWhat's next\n-----------\n\n- Learn more about [BigQuery ML ARIMA_PLUS](/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-time-series).\n- Learn about [the service accounts used by this workflow](/vertex-ai/docs/tabular-data/tabular-workflows/service-accounts#arima)."]]