| === Technical Details === |

The goal of AutoBNN is to create an innovative solution for time series prediction, combining the strengths of BNNs and GPs with compositional kernels.

AutoBNN combines two primary components:
* Bayesian Neural Networks (BNNs): enable neural networks to learn probabilistically and generate output based on the distribution of probability.
* Gaussian Processes (GPs): represent the simplest probabilistic model that can be used for regular functions, with the ability to predict specific values of the process and calculate a estimate of its uncertainty.

This combined approach can be used as a time window algorithm for predicting temporal data.

| === Practical Implications === |

The use of AutoBNN opens up avenues for applications where it is necessary to analyze and predict temporally correlated data.

This can be particularly useful for economic, social, or environmental models that require precise understanding of trends and patterns.

In addition, due to the ability to generate probabilistic output and calculate a estimate of the process' uncertainty, AutoBNN offers greater reliability compared to some traditional algorithms.

Furthermore, its ability to predict specific values of the process and calculate an estimate of its uncertainty makes it particularly useful for applications where precise predictions are required.

| === Conclusion === |

In summary, AutoBNN offers an innovative combination of probabilistic technologies and compositional kernels for time series prediction, with practical implications that can be evaluated in various fields.

With the extension of its kernel functions and greater reliability, AutoBNN is a potentially superior solution to traditional time series prediction methods.

By calculating the process' uncertainty, the use of AutoBNN offers a more comprehensive and precise understanding of trends and patterns in temporal data.