Custom AI Architectures for Predictive Analytics Using Bayesian Statistics and Deep Learning

Abstract

Predictive analytics has emerged as a vital field with significant potential in industries ranging from energy to mobility. As such, it has become a topic of considerable interest for research. Through the use of statistical models, predictive analytics helps reveal patterns and relationships in complex datasets, generating accurate predictions about future events or outcomes. The development of Artificial Intelligence (AI) architectures and data-driven frameworks has further revolutionized the way we perform predictive analytics. However, the broad adoption of AI for predictive analytics is limited due to the lack of custom architectures that can effectively handle the unique complexities of modern datasets and perform robust and accurate predictions. As datasets grow increasingly complex, the need for Bayesian statistics and Deep Learning (DL) in predictive analytics has become increasingly evident. Bayesian statistics offers a versatile framework for incorporating prior knowledge and external knowledge into AI models. This can help mitigate problems such as data sparsity and improve long-term forecasts. Similarly, DL architectures, with their ability to identify and learn complex patterns within datasets, have the potential to unlock new insights and drive innovation in predictive analytics. However, the development of custom AI architectures that leverage such techniques for predictive analytics remains challenging due to their several inherent limitations. This work aims to bridge this research gap by harnessing the power of Bayesian statistics and DL to advance the state-of-the-art in predictive analytics. Specifically, this work proposes custom AI architectures and data-driven frameworks that can (i) perform accurate long-term estimations, (ii) overcome data drift, (iii) provide uncertainty quantifications, (iv) model and predict anomalous behavior, (v) leverage concepts of Design of Experiments, and (vi) perform collaborative modeling. The proposed models and frameworks are evaluated using compelling case studies that demonstrate their effectiveness in improving the accuracy, reliability, and robustness of AI architectures for broader use in predictive analytics.