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<p>Given the complex and prolonged industrial processes involved in rare earth production, including the extraction and separation stages, the utility of short-term price predictions is limited due to the extensive time required to adjust production schedules. Consequently, accurately forecasting the long-term price trends of rare earth products is a pressing challenge. To address this, this paper introduces a VMD–SRF hybrid model tailored for long sequence time-series forecasting (LSTF). To simplify the complexity of the initial data and improve the model’s predictive accuracy, variational mode decomposition (VMD) is first employed to analyze the periodicity and random components in price time series; then, it combines the series random forest model, which is improved based on the random forest (RF) algorithm. Series random forest (SRF) model uses dynamic time warping (DTW) distance as heuristic information to address the deficiencies of random forest in long time series forecasting. This hybrid approach, leveraging the strengths of both VMD and SRF, enhances the handling of LSTF issues. An experimental comparative analysis using four representative datasets of rare earth product prices indicates superior prediction accuracy of the proposed method. These advancements present a promising and applicable strategy for addressing LSTF challenges in various practical settings.</p>

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Long sequence time-series forecasting of rare earth price based on variational mode decomposition and improved random forest

  • 期刊论文

  • 发表日期：

    2025年6月24日

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  • Rongxiu Lu,
  • Kaiyuan Yao,
  • Hui Yang,
  • Wenhao Dai

摘要

Given the complex and prolonged industrial processes involved in rare earth production, including the extraction and separation stages, the utility of short-term price predictions is limited due to the extensive time required to adjust production schedules. Consequently, accurately forecasting the long-term price trends of rare earth products is a pressing challenge. To address this, this paper introduces a VMD–SRF hybrid model tailored for long sequence time-series forecasting (LSTF). To simplify the complexity of the initial data and improve the model’s predictive accuracy, variational mode decomposition (VMD) is first employed to analyze the periodicity and random components in price time series; then, it combines the series random forest model, which is improved based on the random forest (RF) algorithm. Series random forest (SRF) model uses dynamic time warping (DTW) distance as heuristic information to address the deficiencies of random forest in long time series forecasting. This hybrid approach, leveraging the strengths of both VMD and SRF, enhances the handling of LSTF issues. An experimental comparative analysis using four representative datasets of rare earth product prices indicates superior prediction accuracy of the proposed method. These advancements present a promising and applicable strategy for addressing LSTF challenges in various practical settings.