Grey-informed neural network for time-series forecasting
This addresses the challenge of building interpretable neural network models for time-series forecasting in data-scarce scenarios, though it appears incremental as it combines existing grey system theory with neural networks.
The study tackled the problem of neural networks being black-box models requiring large datasets by proposing a grey-informed neural network (GINN) that incorporates prior knowledge from grey system theory to improve interpretability and handle small data samples, resulting in reliable forecasts based on empirical data.
Neural network models have shown outstanding performance and successful resolutions to complex problems in various fields. However, the majority of these models are viewed as black-box, requiring a significant amount of data for development. Consequently, in situations with limited data, constructing appropriate models becomes challenging due to the lack of transparency and scarcity of data. To tackle these challenges, this study suggests the implementation of a grey-informed neural network (GINN). The GINN ensures that the output of the neural network follows the differential equation model of the grey system, improving interpretability. Moreover, incorporating prior knowledge from grey system theory enables traditional neural networks to effectively handle small data samples. Our proposed model has been observed to uncover underlying patterns in the real world and produce reliable forecasts based on empirical data.