Google has introduced a powerful new capability in Connected Sheets for BigQuery: users can now generate forecasts directly inside Google Sheets using BigQuery ML (BQML) and Google’s advanced TimesFM forecasting model
This update removes the need for SQL, Python, or custom model training making machine learning forecasting accessible to business users directly in Google Workspace.
If your organization uses BigQuery and Google Sheets, this changes how forecasting can be done at scale.
Google Sheets users connected to BigQuery can now:
All from within the Sheets interface.
The feature leverages:
TimesFM is pre-trained on billions of real-world data points, allowing organizations to generate sophisticated predictions instantly without building or training their own models. This is a major shift toward no-code machine learning in Google Workspace.
Traditionally, forecasting required:
Now, forecasting can be initiated directly in Google Sheets using a configuration panel dramatically lowering the barrier to entry
This enables:
Without relying on dedicated data scientists for every projection.
According to the official release, the new feature includes
Forecasts can be created from:
All through a user-friendly configuration pane in Sheets. No SQL required.
Users can define:
This provides flexibility without complexity.
The feature supports forecasting across multiple dimensions.
For example:
Multiple time series forecasts can be generated simultaneously
For single time series forecasts, Sheets automatically generates a chart showing:
This accelerates insight delivery for business stakeholders
To use the feature:
There are no admin controls required for this feature
Availability:
Rollout began February 16, 2026.
TimesFM is Google’s foundation model for time series forecasting.
Unlike traditional ML workflows, TimesFM:
This makes forecasting dramatically faster and more accessible to non-technical users. It also signals Google’s broader strategy of embedding foundation models directly into Workspace and Cloud workflows.
This feature is particularly powerful for:
Because it runs directly against BigQuery, it scales to enterprise-sized datasets without manually importing data into Sheets.
This update reduces friction between:
Data teams → Business teams
Instead of:
Business asking Data Science to build models
Business users can:
Self-serve forecasts using governed BigQuery datasets
This improves:
While still keeping data centralized in BigQuery.
Many organizations use:
But fail to fully leverage advanced analytics capabilities.
Suitebriar helps organizations:
If you want to activate no-code forecasting inside Google Sheets or evaluate how BigQuery ML fits your environment, schedule a data strategy session with Suitebriar.