Weighted grey-Markov demand forecasting model with spatial effects

Purpose In order to enhance the prediction accuracy of the grey model in small-sample, high-volatility scenarios and to effectively utilize spatial effects and data autocorrelation features, a weighted grey Markov forecasting model with spatial effects is constructed. Design/methodology/approach A spatial effect module is incorporated to capture inter-regional correlations and heterogeneity, and a weighted Markov chain module is introduced, which adjusts the weights of different state transition steps to better utilize recent data. Findings Experimental results on talent and electricity demand data from multiple regions show that the proposed model significantly outperforms baseline models in prediction accuracy, and ablation studies validate the effectiveness of both the spatial effect module and the weighted Markov chain module. Originality/value Integrating spatial effects into the grey model and leveraging a weighted Markov chain to fully account for the autocorrelation characteristics of the data series, the proposed approach enhances both adaptability and predictive performance in highly volatile scenarios.