This paper proposes a novel Multi-scale Interval-valued Decomposition Ensemble (MIDE) framework for forecasting European Union Allowance (EUA) carbon futures prices, which integrates Noise-assisted Multivariate Empirical Mode Decomposition (NAMEMD), Interval-valued Vector Auto-Regressive (IVAR) model, Interval Event Analysis (IEA) method, and Interval Multi-Layer Perceptron (IMLP). First, the original interval-valued carbon prices with other interval-valued control variables are decomposed and integrated into high, medium, and low-frequency components by NAMEMD. Second, IVAR is used to investigate the dynamics of the interval-valued vector system in low-frequency components, while IMLP is employed to characterize the high-frequency components. Besides, the interval event analysis investigates typical events that significantly impact carbon prices in the medium-frequency component. Furthermore, empirical findings indicate that our proposed MIDE learning approach significantly outperforms some other benchmark models in out-of-sample forecasting.

中文翻译：

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通过多尺度区间值分解集成方法预测区间碳价格


本文提出了一种新的多尺度区间值分解集成 （MIDE） 框架，用于预测欧盟配额 （EUA） 碳期货价格，该框架集成了噪声辅助多元经验模态分解 （NAMEMD）、区间值向量自回归 （IVAR） 模型、区间事件分析 （IEA） 方法和区间多层感知器 （IMLP）。首先，由 NAMEMD 将原始区间值碳价格与其他区间值控制变量分解并整合为高、中、低频分量;其次，IVAR 用于研究区间值矢量系统在低频分量中的动力学，同时采用 IMLP 来表征高频分量。此外，区间事件分析调查了对中频分量碳价格产生显著影响的典型事件。此外，实证结果表明，我们提出的 MIDE 学习方法在样本外预测方面明显优于其他一些基准模型。