核心概念
Implementing a grey-informed neural network enhances interpretability and forecasting accuracy in time-series analysis.
統計
Neural network models have shown outstanding performance and successful resolutions to complex problems in various fields.
The GINN ensures that the output of the neural network follows the differential equation model of the grey system, improving interpretability.
Our proposed model has been observed to uncover underlying patterns in the real world and produce reliable forecasts based on empirical data.
引用
"The GNN presents a promising approach for accurate predictions in various applications." - Chen et al.
"Our proposed model leverages potential underlying laws in the real world to make reasonable predictions based on actual data." - Xie et al.