Prediction and analysis of China’s coastal marine economy: an innovative grey model with the best-matching data-preprocessing techniques

Abstract

China’s coastal marine economy, a key part of the national economy, exhibits complex temporal evolution and regional heterogeneity, posing challenges for accurate forecasting. To address these challenges, this study employs advanced data-preprocessing techniques, accumulating generation operators (AGO) in grey prediction models, to tackle the nonlinear, volatile, and heterogeneous gross ocean product (GOP) data. Specifically, an accumulating generation operator matching mechanism that utilizes a pool of seven advanced AGOs is incorporated into the discrete grey prediction model. The proposed best-matching discrete grey prediction model can accurately describe the GOP system in China’s 11 coastal provinces. Furthermore, the experimental results indicate that the proposed model achieves 5.09% average forecasting mean absolute percentage error, demonstrating 46.65% and 61.73% improvement rates over the single AGO-based and benchmark models, respectively. Consequently, the proposed model is deployed to forecast China’s provincial GOP up to 2025, offering insights into the national development strategies, regionally tailored policies, and inter-provincial coordination in the marine sector.