Google introduced TimesFM-3, a 330-million-parameter time series model that forecasts multiple related data streams simultaneously using a single forward pass. It outperforms competing foundation models on three major benchmarks by incorporating both historical and future-known information (like promotions or weather) to improve prediction accuracy.

• TimesFM-3 handles multivariate forecasting natively—predicting multiple related time series at once while capturing dependencies between them, unlike previous versions limited to single series.
• The model generates entire forecast horizons in one pass using "contiguous patch masking" instead of iterative generation, reducing latency and error accumulation.
• It supports three data types: multiple targets, past-only features, and known-future covariates (like scheduled promotions), letting it incorporate planned events that univariate models miss entirely.
• On Gift-Eval, FEV-Bench, and Time benchmarks, TimesFM-3 ranked first among foundation models for both point and probabilistic forecasting accuracy.

Google published research showing how to reduce token consumption in long AI conversations by 94% using structured state representations instead of keeping the full chat history. This matters because it directly cuts costs for users running extended AI sessions.

• Achieves 94% reduction in token usage during long conversations by replacing full conversation history with a structured representation of current state
• Lower token consumption means significantly cheaper API costs for end users running lengthy AI agent sessions
• Structured state approach maintains performance while drastically improving efficiency in extended interactions

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.