Residual Model

Residual modeling focuses on analyzing and utilizing the discrepancies between predicted and observed values (residuals) to improve model accuracy, enhance interpretability, or achieve specific tasks. Current research emphasizes applications across diverse fields, employing various architectures like neural networks (including ResNets and Transformers), random forests, and Gaussian processes to model and leverage residuals for tasks such as data privacy preservation, image processing, and time series analysis. This approach offers significant potential for improving the performance and robustness of existing models, particularly in complex systems where accurate prediction is challenging, and for gaining insights into underlying data structures and anomalies.