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BigQuery ML ARIMA_PLUS is a univariate forecasting model. As a statistical model, it is faster to train than a model based on neural networks. We recommend training a BigQuery ML ARIMA_PLUS model if you need to perform many quick iterations of model training or if you need an inexpensive baseline to measure other models against.
Like Prophet, BigQuery ML ARIMA_PLUS attempts to decompose each time series into trends, seasons, and holidays, producing a forecast using the aggregation of these models' inferences. One of the many differences, however, is that BQML ARIMA+ uses ARIMA to model the trend component, while Prophet attempts to fit a curve using a piecewise logistic or linear model.
Google Cloud offers a pipeline for training a BigQuery ML ARIMA_PLUS model and a pipeline for getting batch inferences from a BigQuery ML ARIMA_PLUS model. Both pipelines are instances of Vertex AI Pipelines from Google Cloud Pipeline Components (GCPC).
What's nextExcept as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2025-08-15 UTC.
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