Google released TimesFM-3, a 330-million-parameter model that forecasts multiple related time series simultaneously by processing them in a single forward pass. It outperforms competing foundation models on standard benchmarks by learning relationships between different series and incorporating known future events like promotions or weather.

• TimesFM-3 uses a 2D attention mechanism (causal temporal + full variate) that lets the model see patterns across time within a series and correlations between different series at the same time step, enabling it to capture how one series influences another.
• The model generates entire forecast horizons in one pass using "Contiguous Patch Masking" instead of iteratively predicting one step at a time, reducing latency and error accumulation.
• It ranked first on three major benchmarks (Gift-Eval, FEV-Bench, Time) for both point and probabilistic forecasting accuracy against recent competitors like Chronos-2 and Toto 2.0.