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A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting

机译：一种新颖的多尺度非线性集成碳价格预测模型

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摘要

In this study, a novel multiscale nonlinear ensemble leaning paradigm incorporating empirical mode decomposition (EMD) and least square support vector machine (LSSVM) with kernel function prototype is proposed for carbon price forecasting. The EMD algorithm is used to decompose the carbon price into simple intrinsic mode functions (IMFs) and one residue, which are identified as the components of high frequency, low frequency and trend by using the Lempel-Ziv complexity algorithm. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to forecast the high frequency IMFs with ARCH effects. The LSSVM model with kernel function prototype is employed to forecast the high frequency IMFs without ARCH effects, the low frequency and trend components. The forecasting values of all the components are aggregated into the ones of original carbon price by the LSSVM with kernel function prototype-based nonlinear ensemble approach. Furthermore, particle swarm optimization is used for model selections of the LSSVM with kernel function prototype. Taking the popular prediction methods as benchmarks, the empirical analysis demonstrates that the proposed model can achieve higher level and directional predictions and higher robustness. The findings show that the proposed model seems an advanced approach for predicting the high nonstationary, nonlinear and irregular carbon price. (C) 2018 Elsevier B.V. All rights reserved.
机译：在这项研究中，提出了一种新的多尺度非线性集成学习范式，它结合了经验模式分解（EMD）和最小二乘支持向量机（LSSVM）以及核函数原型，用于碳价格预测。 EMD算法用于将碳价分解为简单的固有模式函数（IMF）和一个残基，通过使用Lempel-Ziv复杂度算法将其识别为高频，低频和趋势的组成部分。广义自回归条件异方差（GARCH）模型用于预测具有ARCH效应的高频IMF。具有核函数原型的LSSVM模型用于预测没有ARCH效应的高频IMF，低频和趋势分量。通过基于核函数原型的非线性集成方法，LSSVM将所有组件的预测值汇总到原始碳价中。此外，粒子群优化用于带有内核函数原型的LSSVM的模型选择。实证分析表明，该模型能较好地进行水平和方向性预测，并具有较高的鲁棒性。研究结果表明，提出的模型似乎是一种预测高非平稳，非线性和不规则碳价格的先进方法。 （C）2018 Elsevier B.V.保留所有权利。

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