 A novel non-ferrous metal price hybrid forecasting model based on data preprocessing and error correctionZhichao He and Jianhua Huang Resources Policy, 2023, vol. 86, issue PB Abstract: Accurately forecasting the price of non-ferrous metals is of great significance for traders to avoid risks, enterprises to arrange production plans, and countries to formulate economic policies. In order to improve the forecasting accuracy of non-ferrous metal prices, this paper proposes a novel non-ferrous metal price hybrid forecasting model named IVWAIEE (IVMD-WPD-ARIMA-IELM-ECD). Firstly, the original price series is decomposed into several smoother IMFs using variational mode decomposition (VMD). Simultaneously, the improved sparrow search algorithm (IFASSA) is used to optimize the parameters of VMD to improve the adaptability of VMD. Secondly, wavelet packet decomposition (WPD) is used to decompose the residual sequence generated by VMD to further extract the information in the residual sequence. Then, the components generated by VMD and WPD are defined as high frequency components and low frequency components according to the zero-crossing rate. ARIMA is used to forecast the low frequency components with gentle fluctuations, and extreme learning machine optimized by IFASSA (IELM) is used to forecast the high frequency components with strong fluctuations. The forecasting results of each component are accumulated to obtain the initial forecasting results and error sequence of the non-ferrous metal price. Next, WPD is used to further decompose the error sequence, and the error subsequence is predicted by ARIMA and IELM to obtain the error prediction results. Finally, the error prediction results are used to correct the initial forecasting results, and the final forecasting results of non-ferrous metal prices are obtained. In order to verify the superiority of the proposed model, the copper, aluminum, and zinc futures prices of the London Metal Exchange (LME) are selected as empirical data to verify the model. The results show that the proposed IVWAIEE model has better prediction accuracy and robustness than other benchmark models. Its RMSE values in predicting copper, aluminum, and zinc futures prices are 0.2238, 0.1863, and 0.2137, respectively, and MAE values are 0.1696, 0.1171, and 0.1644, respectively, which are lower than those of other benchmark models; The proposed model not only enriches the application of secondary decomposition and error correction in the field of non-ferrous metal price forecasting, but also solves the problems of insufficient adaptability and underutilization of residual sequence in the traditional variational mode decomposition method; The research results of this paper can provide scientific and effective guidance for the investment, production, and decision-making of non-ferrous metal stakeholders. Keywords: Non-ferrous metals price forecasting; Sparrow search algorithm; Variational mode decomposition; Wavelet packet decomposition; Extreme learning machine (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:86:y:2023:i:pb:s0301420723009005 DOI: 10.1016/j.resourpol.2023.104189 Access Statistics for this article Resources Policy is currently edited by R. G. Eggert More articles in Resources Policy from Elsevier |
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