Description: Temporal Fusion refers to the technique of combining temporal data from various sources for analysis, allowing for a deeper and contextualized understanding of observed phenomena. This methodology is particularly relevant in the fields of artificial intelligence and machine learning, where the ability to integrate and analyze data over time and in various contexts can significantly enhance the accuracy of predictive models. Temporal Fusion relies on technologies such as computer vision and recurrent neural networks (RNNs), which can process sequences of data and recognize patterns over time. Additionally, automation with AI and federated learning enables these processes to be carried out efficiently and at scale, facilitating real-time inference, where devices can make decisions based on fused data. In summary, Temporal Fusion is a powerful tool that allows organizations to extract value from their temporal data, improving decision-making and optimizing processes.
