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BigQuery ML Time-series Forecasting (ARIMA)

By Google
Build Explores that allow business users to create machine learning models for time-series forecasting.
Build Explores that allow business users to create machine learning models for time-series forecasting.

Version

v1.0.3

Release Notes

Category

Blocks

ETL Providers

N/A

SQL Dialects

Google BigQuery and BigQuery ML

Overview

Install this block for free by contacting your Looker admin or visiting your in-product marketplace.

Using this Block, you can integrate Looker with BigQuery ML Time-series (ARIMA Plus) models to get the benefit of forecasting with advanced analytics without needing to be an expert in data science. BigQuery ML ARIMA Plus model includes the following functionality:

  • Infer the data frequency of the time series
  • Handle irregular time intervals
  • Handle duplicate timestamps by taking the mean value
  • Interpolate missing data using local linear interpolation
  • Detect and clean spike and dip outliers
  • Detect and adjust abrupt step (level) changes
  • Detect and adjust holiday effects
  • Detect and adjust for seasonal patterns

This Block gives business users the ability to do time-series forecasting from a new or existing Explore. Explores created with this Block can be used to train multiple time-series models, evaluate them, and access their forecasts in dashboards or custom analyses.

Learn more in the associated BigQuery ML Tutorial.

Step by Step instructions for implementation are in the Block Readme

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