March 28, 2024, 8:53 p.m. | Google AI (noreply@blogger.com)

Google AI Blog ai.googleblog.com

Posted by Urs Köster, Software Engineer, Google Research


Time series problems are ubiquitous, from forecasting weather and traffic patterns to understanding economic trends. Bayesian approaches start with an assumption about the data's patterns (prior probability), collecting evidence (e.g., new time series data), and continuously updating that assumption to form a posterior probability distribution. Traditional Bayesian approaches like Gaussian processes (GPs) and Structural Time Series are extensively used for modeling time series data, e.g., the commonly used Mauna Loa CO2 dataset. …

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