The volatility of crude oil price can significantly impact the stability of the crude oil market and even the global economy. Effectively predicting global crude oil price and volatility provides a scientific basis for decision-making for market regulators and investors worldwide, promoting the sound development of the global economy. In this study, we integrate signal processing with deep learning methods to present an optimal forecasting strategy for global crude oil price and volatility. We select the daily price of the WTI crude oil market from April 4, 1983, to December 12, 2023, for calculating volatility. Subsequently, employing extreme-point symmetric empirical mode decomposition (ESMD), K-means clustering, and fast independent component analysis method, we decompose and reconstruct the forecasting data, obtaining independent components with non-Gaussian characteristics. These components serve as inputs to estimate the accuracy of various models, including BiLSTM, Attention, LSTM, SVR, RF, and their combinations, in predicting crude oil price and volatility from both a point prediction and interval prediction perspective. Empirical results demonstrate that data decomposition, reconstruction, and the BiLSTM-Attention model outperform other models in predicting crude oil price and volatility.

中文翻译：

---

一种提高原油预测精度的新型集成方法：ESMD-CFastICA-BiLSTM-Attention


原油价格的波动会显著影响原油市场的稳定性，甚至全球经济的稳定性。有效预测全球原油价格和波动性，为全球市场监管机构和投资者提供科学决策依据，促进全球经济健康发展。在这项研究中，我们将信号处理与深度学习方法相结合，提出了全球原油价格和波动性的最佳预测策略。我们选择 1983 年 4 月 4 日至 2023 年 12 月 12 日期间 WTI 原油市场的每日价格来计算波动率。随后，采用极值点对称经验模态分解 （ESMD）、K-means 聚类和快速独立分量分析方法，对预测数据进行分解和重建，得到具有非高斯特征的独立分量。这些组件用作输入，以估计各种模型（包括 BiLSTM、Attention、LSTM、SVR、RF 及其组合）从点预测和区间预测角度预测原油价格和波动性的准确性。实证结果表明，数据分解、重建和 BiLSTM-Attention 模型在预测原油价格和波动性方面优于其他模型。