本文選題：碳價(jià)預(yù)測(cè)　切入點(diǎn)：歐盟排放交易體系　出處：《系統(tǒng)工程理論與實(shí)踐》2011年12期

【摘要】：針對(duì)國(guó)際碳市場(chǎng)價(jià)格預(yù)測(cè)LSSVM建模輸入節(jié)點(diǎn)和模型參數(shù)難以確定的問(wèn)題,建立了基于數(shù)據(jù)分組處理方法(GMDH)-粒子群算法(PSO)-最小二乘支持向量機(jī)(LSSVM)的國(guó)際碳市場(chǎng)價(jià)格預(yù)測(cè)模型.首先利用GMDH算法獲得LSSVM建模中的輸入變量;其次應(yīng)用PSO算法對(duì)LSSVM建模中的參數(shù)進(jìn)行優(yōu)化,進(jìn)而使用訓(xùn)練好的LSSVM模型對(duì)測(cè)試樣本進(jìn)行預(yù)測(cè);最后采用該模型對(duì)歐盟排放交易體系(EU ETS)兩個(gè)不同到期時(shí)間的碳期貨價(jià)格(DEC 10和DEC 12)進(jìn)行實(shí)證分析,取得了令人滿(mǎn)意的效果.
[Abstract]:Aiming at the problem that it is difficult to determine the input node and model parameters of LSSVM modeling for international carbon market price prediction, A price prediction model of international carbon market based on data grouping method (GMDH) and particle swarm optimization (PSO) is established. Firstly, the input variables in LSSVM modeling are obtained by using GMDH algorithm. Secondly, PSO algorithm is used to optimize the parameters in LSSVM modeling, and then the trained LSSVM model is used to predict the test samples. Finally, the model is used to analyze the two carbon futures prices with different expiration time (DEC10 and DEC 12) in the EU emissions trading system (EU ETS), and the results are satisfactory.
【作者單位】： 五邑大學(xué)經(jīng)濟(jì)管理學(xué)院;北京理工大學(xué)能源與環(huán)境政策研究中心;北京理工大學(xué)管理與經(jīng)濟(jì)學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(71020107026,70733005) 國(guó)家博士后科學(xué)基金(201104057) 國(guó)家教育部人文社會(huì)科學(xué)青年基金(11YJC630304)
【分類(lèi)號(hào)】：X196
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本文編號(hào)：1691921