Neural CDEs as Correctors for Learned Time Series Models

This article, sourced from ArXiv, likely presents a novel approach to improving the accuracy of time series models. The use of Neural Controlled Differential Equations (CDEs) suggests a focus on modeling the continuous dynamics of time series data. The term "correctors" implies that the CDEs are used to refine or adjust the outputs of existing learned models. The research likely explores how CDEs can be integrated with other machine learning techniques to enhance time series forecasting or analysis.