A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction

Qin, Quande; He, Huangda; Li, Li; He, Lingyun

doi:10.1007/s10614-018-9862-1

Cited by 35 publications

(12 citation statements)

References 47 publications

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“…The results indicated that the ARMA‐type models had better performances on lower frequency and trend components compared with the LSSVMs models (e.g., IMF6‐IMF8 and the residual modes in Table 3), which was different from previous research (Qin et al, 2020; Zhu et al, 2018). The main reasons for these differences in this paper were the summarization of the time series analysis and the design of the ARMA‐type models.…”

Section: Resultscontrasting

confidence: 84%

“…To alleviate this problem, it was necessary to evaluate the effectiveness of the chosen combination. Besides the RMSE, MAE, and MAPE standards, the directional prediction statistic ( D stat ) in the research of Qin et al (2020) was used to measure the directional forecasting performances of the different forecasting methods. The expression for D stat is shown in Equation (18).…”

Section: Empirical Analysismentioning

confidence: 99%

“…First, although hybrid models have been designed to fit with the various carbon price characteristics, such as time variation and nonlinearity, there is no uniform matching process or specific criterion. For example, previous studies have directly used an ARIMA model on the high frequency components and a cointegration model (CIM) on the trend components (Qin et al, 2020; Zhang et al, 2018). However, unsuitable choices for forecasting methods can result in large errors.…”

Section: Introductionmentioning

confidence: 99%

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Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine

Zhang

2021

Journal of Forecasting

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In an attempt to combat global warming, many countries have been introducing carbon trading schemes, which, in turn, has led to increased research interest in carbon price forecasting as accurate predictions can provide valuable decision references for governments and investors. Therefore, this paper proposes an optimal hybrid forecasting framework to ensure accurate long‐term carbon price series predictions for which empirical mode decomposition (EMD) was used to decompose the original data and a novel optimal combination developed to predict each decomposed part, which involved the integration of autoregressive moving average‐type (ARMA‐type) models and least squares support vector machines (LSSVMs) optimized by 10‐fold cross validation and particle swarm optimization (PSO) algorithm. The effectiveness of the optimal EMD‐ARMAs‐LSSVMs framework was then verified using an empirical example from the EU emissions trading system (EU ETS). The long‐term forecast results and comparisons with other forecasting methods demonstrated the stability and validity of the proposed framework. The results indicated that ARMA‐type models are more suitable for low frequency and trend components, and LSSVMs were more suitable for high frequency components. It was also found that the proposed optimal framework was able to effectively reduce the error accumulation.

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Section: Resultscontrasting

confidence: 84%

Section: Empirical Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine

Zhang

2021

Journal of Forecasting

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“…However, the infrastructure of the host country had no significant impact on China's foreign contracted project investment. Scholars [15,16,17] have used the classical gravity model to select countries along the "Silk Road Economic Belt" as samples; added variables, such as the urban population ratio, interest rate, and R&D personnel; and performed empirical regression analysis using the panel data model. Their results indicated that the participation of the host country in a trade agreement, the proportion of urbanization, and the level of urbanization are all important factors affecting the entry of China's contracted projects.…”

Section: Introductionmentioning

confidence: 99%

Impact of natural-resource dependence on foreign contracting projects of China: A spatial panel threshold approach

Sun¹,

Sun²,

Chen³

et al. 2020

PLoS ONE

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The proposal of the "Belt and Road" initiative has had a positive and far-reaching impact on the economic and social development of countries and regions along the route and has provided good opportunities and conditions for the development of China's foreign contracted projects. In the present study, in view of the heterogeneous characteristics and spatial correlation of countries along the Belt and Road, panel data of 46 contracted projects in China along the Belt and Road from 2008 to 2017 were used to empirically study the spatial characteristics of resource heterogeneity and outsourcing projects in the host country from the perspectives of spatial correlation and spatial heterogeneity. The results indicated that China had significant spatial agglomeration effects, natural restraining effects, and spatial spillover effects on the contracting projects along the Belt and Road, and the marginal impact in low-income countries exhibited a "broken line" relationship. Corresponding suggestions were provided for Chinese enterprises contracting projects involving Belt and Road countries. The databases of BRI need to be established, and ensure green investment efficiency.

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“…The finite distributed lag (FDL) model based on a genetic algorithm (GA) has better performance on predicting carbon price than other GARCH models [ 22 ]. Based on the idea of ensemble learning, the EMD model (Empirical Mode Decomposition, EMD) is used to extract the intrinsic mode function (IMF) that represents the different coexisting oscillation modes of carbon series [ 23 , 24 , 25 ], and then a hybrid carbon price forecasting model integrating the variational mode decomposition (VMD) and optimal combination forecasting model (CFM) is constructed, the results suggesting the superiority of the proposed hybrid model for carbon price forecasting [ 26 , 27 ]. Conducting the EMD method, Wang et al [ 28 ] proposed a new random forest-based nonlinear ensemble paradigm for carbon price prediction and proved the model’s superiority in European carbon price forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network

Yun

Zhang

et al. 2022

IJERPH

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The carbon market is recognized as the most effective means for reducing global carbon dioxide emissions. Effective carbon price forecasting can help the carbon market to solve environmental problems at a lower economic cost. However, the existing studies focus on the carbon premium explanation from the perspective of return and volatility spillover under the framework of the mean-variance low-order moment. Specifically, the time-varying, high-order moment shock of market asymmetry and extreme policies on carbon price have been ignored. The innovation of this paper is constructing a new hybrid model, NAGARCHSK-GRU, that is consistent with the special characteristics of the carbon market. In the proposed model, the NAGARCHSK model is designed to extract the time-varying, high-order moment parameter characteristics of carbon price, and the multilayer GRU model is used to train the obtained time-varying parameter and improve the forecasting accuracy. The results conclude that the NAGARCHSK-GRU model has better accuracy and robustness for forecasting carbon price. Moreover, the long-term forecasting performance has been proved. This conclusion proves the rationality of incorporating the time-varying impact of asymmetric information and extreme factors into the forecasting model, and contributes to a powerful reference for investors to formulate investment strategies and assist a reduction in carbon emissions.

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A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction

Cited by 35 publications

References 47 publications

Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine

Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine

Impact of natural-resource dependence on foreign contracting projects of China: A spatial panel threshold approach

Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network

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