Google released TimesFM-3, a 330-million-parameter time series forecasting model trained on over 1 trillion data points that handles multivariate forecasting—predicting multiple related time series simultaneously while incorporating external information. Unlike its predecessors, which only worked with single time series in isolation, TimesFM-3 natively supports multiple targets, past covariates (historical features), and dynamic covariates (known future events like promotions or weather forecasts). The model uses a decoder-only transformer that groups time steps into 32-point patches, then applies two types of attention: causal temporal attention that looks backward within a single series to prevent data leakage, and full variate attention that lets the model see all other series at each time step to learn cross-series correlations. The key technical advance is Contiguous Patch Masking, which generates the entire forecast horizon in one forward pass instead of iteratively predicting one patch at a time. This eliminates latency, reduces error accumulation, and cuts computation. For future events that are known (like scheduled promotions), the model can see those signals; for unknown future values, it masks them. The architecture outputs nine quantiles per target at every time step, giving a probabilistic view of forecast uncertainty rather than just point estimates. In a retail ice cream example, TimesFM-3 correctly anticipated a 20% sales bump on promotion days by learning the relationship between promotions and sales from historical data, while a standard univariate model couldn't account for future promotions at all. On three major benchmarks—Gift-Eval, FEV-Bench, and Time—TimesFM-3 ranked first among all pre-trained foundation models for both point forecasts and probabilistic accuracy. Even in univariate mode (when evaluated without any covariate information), it matched or beat competitors like Chronos-2 and Toto 2.0. The full multivariate mode pushed it further ahead. The model is available on GitHub and Hugging Face now, with BigQuery integration coming in the coming weeks.