# AutoBNN: A New Approach to Time Series Forecasting Scalable and interpretable predictions with Bayesian neural networks. - **Source:** [Google AI Blog](https://ai.googleblog.com) - **Published:** 2024-03-28 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/63f501a5-77a9-4c66-9b68-b6a66a3d3362 ## The Importance of Time Series Forecasting Time series forecasting plays a critical role in various fields such as weather, traffic, and economic trends. Traditional methods rely on manual intervention by experts and high computational costs. AutoBNN offers a more efficient solution by automating these processes. ## Fundamentals of Bayesian Approaches Bayesian methods start with prior assumptions about data patterns and update these assumptions with new data. Traditional Gaussian processes (GPs) and Structural Time Series are examples of such models. However, these methods often require expertise and are computationally expensive. ## AutoBNN's Innovative Approach AutoBNN combines the interpretability of traditional methods with the scalability of neural networks by using Bayesian neural networks (BNNs). This enables automatic model discovery and high-quality uncertainty estimates. ## Compositional Kernel Structure AutoBNN adapts the compositional kernel structure of Gaussian processes to Bayesian neural networks. This structure allows users to create reasonable prior assumptions without needing deep expertise in GPs. ## Advantages of BNNs Bayesian neural networks offer lower computational costs compared to Gaussian processes and integrate better with GPU/TPU hardware. They can also be easily combined with traditional deep BNNs. ## Transition from GP to BNN Researchers have discovered new methods to translate Gaussian process kernels into Bayesian neural networks. This allows leveraging the advantages of GPs while utilizing the flexibility of BNNs. ## Features of the AutoBNN Package AutoBNN is developed using JAX within TensorFlow Probability. It offers basic kernels and operators as well as new kernels and operators. This enables users to create more flexible and learnable models. ## The Future of AutoBNN AutoBNN could set a new standard in time series forecasting. By offering both interpretability and scalability, it may find wide applications in both academic and industrial settings. ## Sources covering this story - http://blog.research.google/2024/03/autobnn-probabilistic-time-series.html — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._