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 next
- Learn more about BigQuery ML ARIMA_PLUS.
- Learn about the service accounts used by this workflow.