An enhanced combined model for NEV sales prediction utilizing complexity self-awareness and sentiment score correction

Accurately predicting new energy vehicle (NEV) sales not only contributes to promoting sustainable mobility but also provides support for resource allocation and infrastructure planning. To address the limitations of existing methods in integrating market sentiment and capturing multiscale data characteristics, this paper proposes a hybrid forecasting model that incorporates complexity self-awareness and sentiment score correction. First, a comprehensive time series dataset is constructed using historical sales data and the corresponding online reviews. Second, the market-oriented sentiment score self-correction (MSSC) algorithm is applied to dynamically optimize sentiment variables. Then, ensemble empirical mode decomposition (EEMD) is used to extract multifrequency components, which are evaluated and classified via the self-aware complexity-driven intelligent quantification (SCIQ) method for refined modelling. An empirical analysis based on BYD Tang NEV sales data demonstrates that the proposed model outperforms state-of-the-art benchmark models across four evaluation metrics. Moreover, after incorporating SCIQ and MSSC, the prediction accuracy improved by 30.04% and 22.34%, respectively, in terms of the RMSE, and by 2.04% and 1.39%, respectively, in terms of the first-order effectiveness. Furthermore, SHAP analysis is employed to interpret key influencing factors, enabling the formulation of consumer-oriented policy recommendations that support high-quality NEV industry development.