AutoBNN: Probabilistic time series forecasting with compositional bayesian neural networks

Favorite 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

Read More
Shared by Google AI Technology March 28, 2024