A New Fractional-Order Grey Prediction Model without a Parameter Estimation Process

Abstract

The fractional-order grey prediction model is widely recognized for its performance in time series prediction tasks with small sample characteristics. However, its parameter-estimation method, namely the least squares method, limits the predictive performance of the model and requires time to address the ill-conditioning of the system. To address these issues, this paper proposes a novel parameter-acquisition method treating structural parameters as hyperparameters, obtained through the marine predators optimization algorithm. The experimental analysis on three datasets validate the effectiveness of the method proposed in this paper.