Crude oil plays an important role in the world economy and contributes to more than one third of energy consumption worldwide. The better forecasting of its fluctuating price is crucial for policymaking. Although various methods had been previously applied for forecasting crude oil price which including both statistical models and Artificial Neural Network models but we find that there is no single paper that compares the AdaBoost-LSTM and AdaBoost-GRU models for improving forecasting performance. We proposed AdaBoost-GRU in which the GRU model was built, put inside sklearn wrapped and finally boost by AdaBoost Regressor. The predictive power of the proposed model was compared with AdaBoost-LSTM using daily Crude oil prices from October 23, 2009, to June 23, 2021, and single LSTM and single GRU were used as the benchmarking models. Forecasting performances were measured using five different metrics namely; the mean absolute error (MAE), the root mean squared error (RMSE), the Scatter Index (SI), the mean absolute percentage error (MAPE), and the weighted mean absolute percentage error (WMAPE). We have demonstrated that the AdaBoost-LSTM and the AdaBoost-GRU models outperform the benchmarking models as expected, and the empirical results show that the AdaBoost-GRU is superior to all models studied in this research.

原油在世界经济中发挥着重要作用，占全球能源消耗的三分之一以上。更好地预测其波动的价格对于决策至关重要。虽然之前已经应用了各种方法来预测原油价格，包括统计模型和人工神经网络模型，但我们发现没有一篇论文比较 AdaBoost-LSTM 和 AdaBoost-GRU 模型以提高预测性能。我们提出了 AdaBoost-GRU，其中构建了 GRU 模型，将其放入 sklearn 包裹中，最后由 AdaBoost Regressor 提升。使用 2009 年 10 月 23 日至 2021 年 6 月 23 日的每日原油价格将所提出模型的预测能力与 AdaBoost-LSTM 进行比较，并使用单个 LSTM 和单个 GRU 作为基准模型。预测性能是使用五种不同的指标来衡量的：平均绝对误差 (MAE)、均方根误差 (RMSE)、散点指数 (SI)、平均绝对百分比误差 (MAPE) 和加权平均绝对百分比误差 (WMAPE)。我们已经证明 AdaBoost-LSTM 和 AdaBoost-GRU 模型的表现优于预期的基准模型，实证结果表明 AdaBoost-GRU 优于本研究中研究的所有模型。