AutoBNN: Automating Time Series Forecasting with Bayesian Neural Networks
分析
This article introduces AutoBNN, a new open-source package from Google Research designed to automate time series forecasting. It addresses the limitations of traditional Bayesian methods (like GPs) which require expert knowledge and can be computationally expensive, as well as the lack of interpretability and reliable uncertainty estimates in standard neural networks. AutoBNN aims to combine the best of both worlds: the interpretability of Bayesian approaches with the scalability and flexibility of neural networks. The article highlights the package's ability to discover interpretable models, provide high-quality uncertainty estimates, and scale to large datasets. The mention of JAX suggests a focus on performance and automatic differentiation capabilities.
要点
- •AutoBNN is a new open-source package for time series forecasting.
- •It combines Bayesian methods with neural networks for improved interpretability and scalability.
- •It automates model discovery and provides uncertainty estimates.
“AutoBNN automates the discovery of interpretable time series forecasting models, provides high-quality uncertainty estimates, and scales effectively for use on large datasets.”