Google Research released TimesFM-3, a 330M-parameter zero-shot foundation model for multivariate time series forecasting that predicts multiple related series in a single forward pass. It was pretrained on 1T+ time points and outputs 9 quantiles per horizon step. The model becomes usable for benchmarking with released code, but its weights are restricted to non-commercial, non-production use so it can’t be shipped behind a production forecasting API.
Google launched TimesFM-3, a 330-million-parameter forecasting model for multivariate time-series prediction. It was trained on over 1 trillion real-world and synthetic time points and released on Hugging Face under a non-commercial license. As a result, it can be used for non-commercial, non-production work while commercial access will require paid routes, such as when BitQuery swaps to it for its AI.FORECAST command.
Z.ai reported first-half results after going public in Hong Kong, showing revenue growth alongside ongoing losses. Revenue rose about 400% to 953.89 million yuan (about $141.8 million), but it missed analysts’ average 1.35 billion yuan estimate by roughly 29%. The gap and high price-to-sales valuation are tied to competition and the company’s continued capital raising, including a $4 billion share placement, as it presses spending on its AI model lineup.
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