TimesFM-3 model for multivariate time series forecasting

Google Research has introduced TimesFM-3, a state-of-the-art time series foundation model that enables accurate multivariate forecasting in a single forward pass.

Moving beyond univariate forecasting

Previous versions of the model, up to TimesFM-2.5 released in September 2025, were strictly limited to univariate forecasting. This meant they could only use the history of a single time series to predict the future. However, most real-world scenarios are multivariate, where multiple time series and auxiliary external features jointly impact future outcomes.

For example, predicting retail ice cream sales is more accurate when drawing on sales of related products, historical foot traffic, and known future events like weather forecasts and promotions.

Architecture and inference

TimesFM-3 features 330 million parameters and is pre-trained on a corpus comprising more than 1 trillion time points. The model builds on a decoder-only transformer architecture — a neural network design that processes data sequentially using attention mechanisms.