A New Two-Stage Approach with Boosting and Model Averaging for Interval-Valued Crude Oil Prices Forecasting in Uncertainty Environments
A New Two-Stage Approach with Boosting and Model Averaging for Interval-Valued Crude Oil Prices Forecasting in Uncertainty Environments
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DOI：
10.3389/fenrg.2021.707937
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发表时间：
2021-08-19
影响因子：
3.4
通讯作者：
Wang, Shouyang
中科院分区：
文献类型：
--
作者：
Huang, Bai;Sun, Yuying;Wang, Shouyang
鉴于石油市场的内在复杂性，原油价格受到众多因素的影响，这使得预测非常困难。认识到这一挑战，已经引入了许多方法，但很少有关于区间价值价格的工作。为了捕捉原油价格变动的潜在特征，本文提出了一种两阶段预测方法来预测区间值时间序列，该方法将点值预测推广到不确定性和变异性。实证结果表明，本文提出的方法在预测精度和稳健性分析方面都明显优于所有基准模型。研究结果可为决策者了解原油价格走势，提高经济活动效率提供参考。
In view of the intrinsic complexity of the oil market, crude oil prices are influenced by numerous factors that make forecasting very difficult. Recognizing this challenge, numerous approaches have been introduced, but little work has been done concerning the interval-valued prices. To capture the underlying characteristics of crude oil price movements, this paper proposes a two-stage forecasting procedure to forecast interval-valued time series, which generalizes point-valued forecasts to incorporate uncertainty and variability. The empirical results show that our proposed approach significantly outperforms all the benchmark models in terms of both forecasting accuracy and robustness analysis. These results can provide references for decision-makers to understand the trends of crude oil prices and improve the efficiency of economic activities.