AutoBNN: Probabilistic time series forecasting with compositional bayesian neural networks
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AutoBNN combines the interpretability of traditional probabilistic approaches with the scalability and flexibility of neural networks for building sophisticated time series prediction models using complex data.
AutoBNN: Probabilistic time series forecasting with compositional bayesian neural networks March 28, 2024Posted by Urs Köster, Software Engineer, Google Research AutoBNN combines the interpretability of traditional probabilistic approaches with the scalability and flexibility of neural networks for building sophisticated time series prediction models using complex data. Quick links AutoBNN package Share Copy link × 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. However, they often rely on domain experts to painstakingly select appropriate model components and may be computationally expensive. Alternatives such as neural networks lack interpretability, making it difficult to understand how they generate forecasts, and don't produce reliable confidence intervals.To that end, we introduce AutoBNN, a new open-source package written in JAX. AutoBNN automates the discovery of interpretable time series forecasting models, provides high-quality uncertainty estimates, and scales effectively for use on l...