Purpose Accurate energy data prediction holds significant practical importance for optimizing energy structures and informing governmental energy decision making. Due to the uncertainty inherent in energy sequences, this paper proposes a hybrid model combining seasonal–trend decomposition and a nonlinear time-delay grey model. Design/methodology/approach The trend variations inherent in the data are obtained through signal decomposition techniques, and higher weights are assigned to new information within the model. Prediction results are restored via seasonal factors, and the optimal parameter values are obtained using the PSO algorithm. Findings An analysis of the model’s various characteristics is conducted. By fitting and forecasting China’s natural gas production data and comparing the results with those of four other benchmark models, the validity of the model is verified. Originality/value This study extends trend decomposition and seasonal factor restoration by incorporating nonlinear terms, lagged terms and new information priority, enabling the model to better adapt to dynamic trend changes and significantly improve prediction accuracy for time series with prominent dynamic trends and seasonal variations.
Yan et al. (Mon,) studied this question.
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