A nonlinear mixed-frequency grey prediction model with two-stage lag parameter optimization and its application

Abstract

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.With the advancement of data science, the demand for methods capable of simultaneously processing and utilizing complex mixed-frequency data systems with uncertainty characteristics is increasing. To address this need, a novel nonlinear mixed-frequency grey prediction model with two-stage lag parameter optimization is proposed which integrates frequency-domain analysis and optimization algorithm. The proposed model innovatively incorporates the phase spectrum analysis method into the mixed-frequency modeling framework, determines a reasonable range for lag parameters using frequency-domain analysis, and enhances the characterization of system nonlinearity by introducing a power-driven term. The effectiveness and robustness of the proposed model are validated through both experiments on synthetic data and real-world case studies on electricity consumption. Comparative experiments against existing mixed-frequency grey prediction model, nonlinear grey prediction model, and mixed-frequency sampling regression model demonstrate that proposed model exhibits superior performance in key metrics, including mean absolute percentage error and standard deviation. This study provides a novel solution for modeling relationships among multi-frequency variables in complex systems