Google新出的TimesFM-3能一次性预测多个时间序列，不用专门微调，在多个基准测试中排名第一。
Google Research发布了TimesFM-3，拥有3300万参数的时间序列基础模型。该模型可在单次前向传播中预测多个相关时间序列，原生支持多元预测。在GIFT-Eval、fev-bench和TIME排行榜上，TimesFM-3在预训练基础模型中平均排名第一。模型权重采用非商业、非生产许可发布。
Google AI Releases TimesFM-3: A 330M Parameter Zero-Shot Foundation Model For Multivariate Time Series Forecasting
Google Research has released TimesFM-3, a 330 million parameter time series foundation model that forecasts multiple related series in a single forward pass. Unlike every TimesFM checkpoint through 2.5, it is pretrained natively for multivariate forecasting, accepting multiple targets, past covariates, and past-future covariates with no task-specific fine-tuning. It takes the top average rank among pretrained foundation models on GIFT-Eval, fev-bench, and the TIME leaderboard. The weights, however, ship under a non-commercial, non-production license. The post Google AI Releases TimesFM-3: A 330M Parameter Zero-Shot Foundation Model For Multivariate Time Series Forecasting appeared first on MarkTechPost .