TimesFM models in BigQuery and AlloyDB

We are thrilled to announce the integration of TimesFM into our leading data platforms, BigQuery and AlloyDB. This brings the power of large-scale, pre-trained forecasting models directly to your data within the Google Data Cloud, enabling you to predict future trends with unprecedented ease and accuracy.

TimesFM is a powerful time-series foundation model developed by Google Research, pre-trained on a vast dataset of over 400 billion real-world time-points. This extensive training allows TimesFM to perform “zero-shot” forecasting, meaning it can generate accurate predictions for your specific data without needing to be retrained. This dramatically simplifies the process of creating and deploying forecasting models, saving you time and resources.

Now, let’s dive into what this means for you in BigQuery and AlloyDB.

TimesFM in BigQuery

We launched the AI.FORECAST function in preview at Google Cloud Next ‘25. Today, we are announcing: 

Let’s take a look at these in greater depth. 

AI.FORECAST and AI.EVALUATE

The GA launch includes major upgrades:

• TimesFM 2.5 is now supported. By specifying `model => “TimesFM 2.5”`, you can use the latest TimesFM model to achieve better forecasting accuracy and lower latency.

• AI.FORECAST supports dynamic context windows up to 15K: Multiple context windows from 64 to 15K are supported, by specifying `context_window`. If not specified, a context window is selected to match the time series input size.

• AI.FORECAST supports displaying historical data: Displaying historical data together with forecasts is supported by setting `output_historical_time_series` to true. The option enhances usability by enabling easier and better visualizations.

• We add AI.EVALUATE for model evaluation. Users can specify the actual data to evaluate the accuracy of the forecasted value.

In this example, you can use the TimesFM 2.5 model and specify the context window = 1024 in AI.FORECAST to use the latest 1024 points as the history data. You can specify output_historical_time_series = true to display historical data together with the forecasts.