A Novel Gaussian Process Approach for Prediction of University Academic Evaluation

In the globalized academic landscape, university academic evaluation plays a crucial role. However, predicting university academic evaluation in time series prediction poses significant challenges due to its nonlinearity and nonstationarity. In this article, we propose a novel method to address the prediction problem in scenarios characterized by limited data volume. By leveraging the properties of Gaussian processes, our method constructs nonstationary kernels that are fully trainable. The time series data is decomposed into trend and seasonal components, which are then inputted into an independent Gaussian process model equipped with trainable parameters. The core of our method lies in the trend Gaussian process model, which consists of classical kernel functions and multilayer perceptron stacks. This combination enhances the model’s capacity to capture long-term dependencies and improves prediction accuracy. To evaluate the effectiveness of our novel method, experiments were conducted on both the M4 dataset and an academic indicator dataset. The experimental results demonstrate significant improvements in prediction accuracy and stability compared with traditional time series prediction methods. Additionally, comparative experiments were performed to contrast our method against other popular time series prediction methods, consistently affirming the superior predictive performance of our approach.