A reliable crude oil price forecast is important for market pricing. Despite the widespread use of ensemble empirical mode decomposition (EEMD) in financial time series forecasting, the one-time decomposition on the entire time series leads the in-sample data to be affected by the out-of-sample data. Consequently, the forecasting accuracy is overstated. This study incorporates a rolling window into two prevalent EEMD-based modeling paradigms, namely decomposition-ensemble and denoising, to ensure that only in-sample time series is processed by EEMD and used for model training. Given the time-consuming process of stepwise preprocessing and model fitting, two non-iterative machine learning algorithms, random vector functional link (RVFL) neural network and extreme learning machine (ELM), are used as predictors. Hence, we develop the rolling decomposition-ensemble and rolling denoising paradigms, respectively. Contrary to the majority of prior studies, empirical results based on monthly spot price time series for the Brent and West Texas Intermediate (WTI) markets indicate that EEMD plays a weak role in improving crude oil price forecasts when only the in-sample set is preprocessed. This is compatible with the weak form of the efficient market hypothesis (EMH). Nevertheless, the suggested rolling EEMD-denoising model has an advantage over other employed models for long-term forecasting.

可靠的原油价格预测对于市场定价非常重要。尽管在金融时间序列预测中广泛使用集合经验模态分解（EEMD），但对整个时间序列的一次性分解导致样本内数据受到样本外数据的影响。因此，预测的准确性被夸大了。本研究将滚动窗口纳入两种流行的基于 EEMD 的建模范例，即分解集成和去噪，以确保只有样本内时间序列由 EEMD 处理并用于模型训练。鉴于逐步预处理和模型拟合的耗时过程，两种非迭代机器学习算法，随机向量函数链接（RVFL）神经网络和极限学习机（ELM）被用作预测器。因此，我们分别开发了滚动分解集成和滚动去噪范例。与大多数先前的研究相反，基于布伦特和西德克萨斯中质原油 (WTI) 市场每月现货价格时间序列的实证结果表明，当仅对样本集进行预处理时，EEMD 在改善原油价格预测方面的作用较弱. 这与有效市场假说 (EMH) 的弱形式相容。尽管如此，建议的滚动 EEMD 去噪模型在长期预测方面优于其他采用的模型。基于布伦特和西德克萨斯中质原油 (WTI) 市场每月现货价格时间序列的实证结果表明，当仅对样本集进行预处理时，EEMD 在改善原油价格预测方面的作用较弱。这与有效市场假说 (EMH) 的弱形式相容。尽管如此，建议的滚动 EEMD 去噪模型在长期预测方面优于其他采用的模型。基于布伦特和西德克萨斯中质原油 (WTI) 市场每月现货价格时间序列的实证结果表明，当仅对样本集进行预处理时，EEMD 在改善原油价格预测方面的作用较弱。这与有效市场假说 (EMH) 的弱形式相容。尽管如此，建议的滚动 EEMD 去噪模型在长期预测方面优于其他采用的模型。