Google's new forecasting model beats everyone. You can't use it at work (yet).
5 Articles
5 Articles
Google's new forecasting model beats everyone. You can't use it at work (yet).
On Monday, Google launched TimesFM-3, a 330-million-parameter time-series forecasting model trained on over a trillion real-world and synthetic data time The post Google’s new forecasting model beats everyone. You can’t use it at work (yet). appeared first on The New Stack.
Google’s New TimesFM-3 Outperforms Rivals At Complex Forecasting And Will Soon Be Available In BigQuery
Google releases TimesFM-3, the first natively multivariate model in the series, achieving SOTA benchmarks with BigQuery integration arriving soon. The post Google’s New TimesFM-3 Outperforms Rivals At Complex Forecasting And Will Soon Be Available In BigQuery appeared first on Metaverse Post.
Google Research has released "TimesFM-3," an AI model that predicts the future from time series data. TimesFM-3 is an AI that can read multiple data points that change over time, such as sales, customer traffic, and weather, and predict future values. It is pre-trained on a massive amount of time series data containing over one trillion data points, and is characterized by its ability to be used without additional training. Read more...
Google Research presented TimesFM-3, a foundational model of 330 million parameters designed to forecast several related time series in a single pass. The tool incorporates historical signals and future variables known without fine tuning, but its weights maintain restrictions that prevent commercial and productive use.
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-…
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