基于非结构数据流行学习的碳价格多尺度组合预测
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摘要
碳交易价格的有效预测对制定符合国情的碳金融市场政策以及碳金融市场的风险管理都具有重要意义.对此,提出一种基于非结构数据流行学习的碳价格多尺度组合预测方法.首先,利用网络搜索指数提取碳价格相关的非结构化数据,基于等度量映射流行学习对其进行降维;然后,对降维后的非结构化数据、其他影响因素结构化数据、碳交易价格分别进行经验模态分解(Empirical mode decomposition, EMD),得到不同个数的本征模函数(Intrinsic mode function, IMF),并采用Fine-to-coarse方法对IMF进行重构,得到高频序列、低频序列和趋势项;最后,利用自回归积分滑动平均模型(Autoregressive integrated moving average model, ARIMA)、偏最小二乘(Partial least squares, PLS)回归和神经网络对高频数据、低频数据和趋势项进行预测,将3种预测结果进行集成,得到最终预测值.仿真实验结果表明,所提出的方法可以有效利用多源信息,具有较高的预测精度和良好的适用性.
Forecasting carbon trading price effectively is of great significance for the formulation of carbon financial market policies that suit to Chinese condition and the risk management of carbon financial market. This paper proposes a multi-scale combined forecast method for carbon price based on manifold learning of unstructured data. Firstly, the unstructured data related to carbon price is extracted using the network search index, and dimensionality reduction is performed based on the isometric mapping manifold learning. Then, the structured data of other influencing factors and the carbon trading price are decomposed into a variable number of intrinsic mode functions(IMFs) using empirical mode decomposition(EMD) respectively. The IMF is reconstructed to get high frequency sequence, low frequency sequence and trend item based on the fine-to-coarse method. Moreover, autoregressive integrated moving average model(ARIMA),Partial least squares(PLS) regression and neural network are used to forecast high-frequency data, low-frequency data and trend items, which are aggregated to get the final forecast result. The results of simulated experiments show that the proposed method can effectively use multi-source information and has high prediction accuracy and good applicability.
Forecasting carbon trading price effectively is of great significance for the formulation of carbon financial market policies that suit to Chinese condition and the risk management of carbon financial market. This paper proposes a multi-scale combined forecast method for carbon price based on manifold learning of unstructured data. Firstly, the unstructured data related to carbon price is extracted using the network search index, and dimensionality reduction is performed based on the isometric mapping manifold learning. Then, the structured data of other influencing factors and the carbon trading price are decomposed into a variable number of intrinsic mode functions(IMFs) using empirical mode decomposition(EMD) respectively. The IMF is reconstructed to get high frequency sequence, low frequency sequence and trend item based on the fine-to-coarse method. Moreover, autoregressive integrated moving average model(ARIMA),Partial least squares(PLS) regression and neural network are used to forecast high-frequency data, low-frequency data and trend items, which are aggregated to get the final forecast result. The results of simulated experiments show that the proposed method can effectively use multi-source information and has high prediction accuracy and good applicability.
引文
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[19]董倩,孙娜娜,李伟.基于网络搜索数据的房地产价格预测[J].统计研究,2014,31(10):81-88.(Dong Q,Sun N N,Li W.Real estate price prediction based on web search data[J].Statistical Research,2014,31(10):81-88.)
[20]王炼,宁一鉴,贾建民.基于网络搜索的销量与市场份额预测:来自中国汽车市场的证据[J].管理工程学报,2015,29(4):56-64.(Wang L,Ning Y J,Jia J M.Predicting sales and market share with online search:evidence from Chinese automobile market[J].J of Industrial Engineering and Management,2015,29(4):56-64.)
[21]陈植元,米雁翔,厉洋军,等.基于百度指数的投资者关注度与股票市场表现的实证分析[J].统计与决策,2016(23):155-157.(Chen Z Y,Mi Y X,Li Y J,et al.Investor concern based on the Baidu index an empirical analysis of the performance of the stock market[J].Statistics&Decision,2016(23):155-157.)
[22]颜七笙,王士同,张延飞,等.基于经验模式分解和极限学习机的铀资源价格预测方法[J].控制与决策,2014,29(7):1187-1192.(Yan Q S,Wang S T,Zhang Y F,et al.Uranium resource price prediction based on empirical mode decomposition and extreme learning machine[J].Control and Decision,2014,29(7):1187-1192.)
[23]陈华友.组合预测方法有效性理论及其应用[M].北京:科学出版社,2008:60-64.(Chen H Y.The theory of the effectiveness of combined forecasting method and its application[M].Beijing:Science Press,2008:60-64.)
[24]陈晓红,王陟昀.碳排放权交易价格影响因素实证研究--以欧盟排放交易体系(EUETS)为例[J].系统工程,2012,30(2):53-60.(Chen X H,Wang Z Y.Empirical research on price impact factor of carbon emission exchange:Evidence from EUETS[J].Systems Engineering,2012,30(2):53-60.)
[25]Tenenbaum J B,De S V,Langford J C.A global geometric framework for nonlinear dimensionality reduction[J].Science,2000,290(5500):2319-2322.
[2]Chevallier J.Nonparametric modeling of carbon prices[J].Energy Economics,2011,33(6):1267-1282.
[3]张跃军,魏一鸣.国际碳期货价格的均值回归:基于EU ETS的实证分析[J].系统工程理论与实践,2011,31(2):214-220.(Zhang Y J,Wei Y M.Interpreting the mean reversion of international carbon futures price:Empirical evidence from the EU ETS[J].Systems Engineering-Theory&Practice,2011,31(2):214-220.)
[4]张晨,杨仙子.基于改进的Grey-Markov对区域碳排放市场价格的预测[J].统计与决策,2016,31(9):92-95.(Zhang C,Yang X Z.Prediction of regional carbon market price based on improved grey-Markov[J].Statistics&Decision,2016,31(9):92-95.)
[5]Zhu B,Wei Y.Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology[J].Omega,2013,41(3):517-524.
[6]Fan X,Li S,Tian L.Chaotic characteristic identification for carbon price and an multi-layer perceptron network prediction model[J].Expert Systems with Applications,2015,42(8):3945-3952.
[7]Byun S J,Cho H.Forecasting carbon futures volatility using GARCH models with energy volatilities[J].Energy Economics,2013,40(2):207-221.
[8]刘金培,林盛,郭涛,等.一种非线性时间序列预测模型及对原油价格的预测[J].管理科学,2011,24(6):104-112.(Liu J P,Lin S,Guo T,et al.Nonlinear time series forecasting model and its application for oil price forecasting[J].J of Management Science,2011,24(6):104-112.)
[9]张晨,杨仙子.基于多频组合模型的中国区域碳市场价格预测[J].系统工程理论与实践,2016,36(12):3017-3025.(Zhang C,Yang X Z.Price prediction of China’s regional carbon market based on multi frequency combination model[J].Systems Engineering-Theory&Practice,2016,36(12):3017-3025.)
[10]王书平,朱艳云.基于多尺度分析的小麦价格预测研究[J].中国管理科学,2016,24(5):85-91.(Wang S P,Zhu Y Y.Forecasting of wheat price based on multi-scale analysis[J].Chinese J of Management Science,2016,24(5):85-91.)
[11]Zhu B,Han D,Wang P,et al.Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression[J].Applied Energy,2017,191(8):521-530.
[12]Li W,Lu C.The research on setting a unified interval of carbon price benchmark in the national carbon trading market of China[J].Applied Energy,2015,155:728-739.
[13]高杨,李健.基于EMD-PSO-SVM误差校正模型的国际碳金融市场价格预测[J].中国人口、资源与环境,2014,24(6):163-170.(Gao Y,Li J.Int carbon finance market price prediction based on EMD-PSO-SVM error correction model[J].China Population,Resources and Environment,2014,24(6):163-170.)
[14]张国兴,张振华,刘鹏,等.我国碳排放增长率的运行机理及预测[J].中国管理科学,2015,23(12):86-93.(Zhang G X,Zhang Z H,Liu P,et al.The running mechanism and prediction of the growth rate of China’s carbon emissions[J].Chinese J of Management Science,2015,23(12):86-93.)
[15]赵立祥,胡灿.我国碳排放权交易价格影响因素研究--基于结构方程模型的实证分析[J].价格理论与实践,2016(7):101-104.(Zhao L X,Hu C.Study on the influence factors of carbon emission trading price in China:An empirical analysis based on structural equation model[J].Price:Theory&Practice,2016(7):101-104.)
[16]Rausch S,Metcalf G E,Reilly J M.Distributional impacts of carbon pricing:A general equilibrium approach with micro-data for households[J].Energy Economics,2011,33(6):S20-S33.
[17]王娜.基于大数据的碳价预测[J].统计研究,2016,33(11):56-62.(Wang N.Forecasting of carbon price based on big data[J].Statistical Research,2016,33(11):56-62.)
[18]Ginsberg J,Mohebbi M H,Patel R S,et al.Detecting influenza epidemics using search engine query data[J].Nature,2009,457(7232):1012-1014.
[19]董倩,孙娜娜,李伟.基于网络搜索数据的房地产价格预测[J].统计研究,2014,31(10):81-88.(Dong Q,Sun N N,Li W.Real estate price prediction based on web search data[J].Statistical Research,2014,31(10):81-88.)
[20]王炼,宁一鉴,贾建民.基于网络搜索的销量与市场份额预测:来自中国汽车市场的证据[J].管理工程学报,2015,29(4):56-64.(Wang L,Ning Y J,Jia J M.Predicting sales and market share with online search:evidence from Chinese automobile market[J].J of Industrial Engineering and Management,2015,29(4):56-64.)
[21]陈植元,米雁翔,厉洋军,等.基于百度指数的投资者关注度与股票市场表现的实证分析[J].统计与决策,2016(23):155-157.(Chen Z Y,Mi Y X,Li Y J,et al.Investor concern based on the Baidu index an empirical analysis of the performance of the stock market[J].Statistics&Decision,2016(23):155-157.)
[22]颜七笙,王士同,张延飞,等.基于经验模式分解和极限学习机的铀资源价格预测方法[J].控制与决策,2014,29(7):1187-1192.(Yan Q S,Wang S T,Zhang Y F,et al.Uranium resource price prediction based on empirical mode decomposition and extreme learning machine[J].Control and Decision,2014,29(7):1187-1192.)
[23]陈华友.组合预测方法有效性理论及其应用[M].北京:科学出版社,2008:60-64.(Chen H Y.The theory of the effectiveness of combined forecasting method and its application[M].Beijing:Science Press,2008:60-64.)
[24]陈晓红,王陟昀.碳排放权交易价格影响因素实证研究--以欧盟排放交易体系(EUETS)为例[J].系统工程,2012,30(2):53-60.(Chen X H,Wang Z Y.Empirical research on price impact factor of carbon emission exchange:Evidence from EUETS[J].Systems Engineering,2012,30(2):53-60.)
[25]Tenenbaum J B,De S V,Langford J C.A global geometric framework for nonlinear dimensionality reduction[J].Science,2000,290(5500):2319-2322.