基于混合模型的国际原油价格预测研究
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摘要
由于国际原油价格的剧烈波动,使得准确的原油价格预测极具挑战。为此,提出一种基于变分模态分解、季节性差分自回归滑动平均模型和果蝇优化最小二乘支持向量机的混合模型。利用变分模态分解方法将国际原油价格序列分解成一系列模态分量;针对周期性和非线性特征分量,分别建立季节性差分自回归滑动平均模型和果蝇优化最小二乘支持向量机模型进行预测;将各分量的预测值求和作为最终的预测结果。实证研究结果表明:所提混合模型相较对比模型能够明显提高国际原油价格的预测精度。
Due to the serious fluctuations of international crude oil price, to accurately forecast crude oil price is very challenging.Therefore,a hybrid model based on variational mode decomposition(VMD),seasonal autoregressive integrated moving average(SARIMA)and least squares support vector machine(LSSVM)optimized by fruit fly optimization algorithm(FOA)was proposed.First, the international crude oil price series was decomposed into some mode components by VMD. Then,the SARIMA and LSSVM-FOA were established for periodic and nonlinear components, respectively. Finally, the summation of the forecast values of each component was used as the final forecasting result. Empirical results show that the proposed hybrid model can significantly improve the forecasting accuracy of international crude oil price compared with other models.
Due to the serious fluctuations of international crude oil price, to accurately forecast crude oil price is very challenging.Therefore,a hybrid model based on variational mode decomposition(VMD),seasonal autoregressive integrated moving average(SARIMA)and least squares support vector machine(LSSVM)optimized by fruit fly optimization algorithm(FOA)was proposed.First, the international crude oil price series was decomposed into some mode components by VMD. Then,the SARIMA and LSSVM-FOA were established for periodic and nonlinear components, respectively. Finally, the summation of the forecast values of each component was used as the final forecasting result. Empirical results show that the proposed hybrid model can significantly improve the forecasting accuracy of international crude oil price compared with other models.
引文
[1]徐凌,黎佳卉,李亮. ARIMA模型在国际原油价格预测中的应用[J].河南科学,2013,31(5):692-695.
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[13] FANG T T,LAHDELMA T. Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system[J]. Applied Energy,2016,179:544-552.
[14] TADESSE K,DINKA M. Application of SARIMA model to forecasting monthly flows in Waterval River,South Africa[J]. Journal of Water and Land Development,2017,35(1):229-236.
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[16] YAN X,CHOWDHURY N A. Mid-term electricity market clearing price forecasting:a hybrid LSSVM and ARMAX approach[J].International Journal of Electrical Power and Energy Systems,2013,53:20-27.
[17] SHAYEGHI H,GHASEMI A.Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based scheme[J]. Energy Conversion and Management,2013,74:482-492.
[18]李思慧,刘海卿.基于LMD-PSO-LSSVM组合模型的深基坑变形预测[J].地下空间与工程学报,2018(2):483-489.
[19] KONSTANTIN D,DOMINIOUE Z. Variation mode decomposition[J]. IEEE Transaction on Signal Processing,2014,62(3):531-544.
[20]夏天,程细玉. SARIMA模型的建模及其信贷预测研究[J].华侨大学学报(自然科学版),2006,27(3):332-335.
[21] LONG B,XIAN W X,LI M,et al. Improved diagnostics for the incipient faults in analog circuits using LSSVM based on PSO algorithm with Mahalanobis distance[J]. Neurocomputing,2014,133:237-248.
[22] KEERTHI S S,LIN C J. Asymptotic behaviors of support vector machines with gaussian kernel[J]. Neural Computation,2003,15(7):1667-1689.
[23] SUYKENS J A K,VANDEWALLE J. Least squares support vector machine classifiers[J]. Neural Processing Letters,1999,9(3):293-300.
[24] HU R,WEN S P,ZENG Z G,et al. A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm[J]. Neurocomputing,2017,221:24-31.
[25]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.
[26] CHEN Y H,ZHANG C A,HE K J,et al. Multi-step-ahead crude oil price forecasting using a hybrid grey wave model[J].Physica A:Statistical Mechanics and its Applications,2018,501:98-110.
[2]包湘海,李思敏,顾海兵.国际原油价格长期趋势预测——基于周期分析的视角[J].价格理论与实践,2014(12):59-62.
[3]熊熊,李璇.国际原油价格预测研究[J].重庆理工大学学报(自然科学版),2016,30(1):48-53.
[4] SHIN H,HOU T,PARK K,et al. Prediction of movement direction in crude oil prices based on semi-supervised learning[J].Decision Support Systems,2013,55:348-358.
[5]范秋枫,王涛,张智峰.量子粒子群智能算法在国际布伦特原油价格预测中的应用[J].模糊系统与数学,2017(4):84-90.
[6] YU L,XU H J,TANG L. LSSVR ensemble learning with uncertain parameters for crude oil price forecasting[J]. Applied Soft Computing,2017,56:692-701.
[7] ZHAO Y,LI J P. A deep learning ensemble approach for crude oil price forecasting[J]. Energy Economics,2017,66:9-16.
[8]张金良,谭忠富.基于混合模型的原油价格混沌预测方法[J].运筹与管理,2013,22(5):166-172.
[9]杨云飞,鲍玉昆,胡忠义,等.基于EMD和SVMs的原油价格预测方法[J].管理学报,2015,7(12):1884-1889.
[10] ZHANG J L,ZHANG Y J,ZHANG L. A novel hybrid method for crude oil price forecasting[J]. Energy Economics,2015,49:649-659.
[11] AZEVEDO V G,CAMPOS L M S. Combination of forecasts for the price of crude oil on the spot market[J]. International Journal of Production Research,2016,54:1-17.
[12] SAFARI A,DAVALLOU M. Oil price forecasting using a hybrid model[J]. Energy,2018,148:49-58.
[13] FANG T T,LAHDELMA T. Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system[J]. Applied Energy,2016,179:544-552.
[14] TADESSE K,DINKA M. Application of SARIMA model to forecasting monthly flows in Waterval River,South Africa[J]. Journal of Water and Land Development,2017,35(1):229-236.
[15]金玮.基于周期性分量提取的城市快速路交通流短时预测理论与方法研究[D].北京:北京交通大学,2017.
[16] YAN X,CHOWDHURY N A. Mid-term electricity market clearing price forecasting:a hybrid LSSVM and ARMAX approach[J].International Journal of Electrical Power and Energy Systems,2013,53:20-27.
[17] SHAYEGHI H,GHASEMI A.Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based scheme[J]. Energy Conversion and Management,2013,74:482-492.
[18]李思慧,刘海卿.基于LMD-PSO-LSSVM组合模型的深基坑变形预测[J].地下空间与工程学报,2018(2):483-489.
[19] KONSTANTIN D,DOMINIOUE Z. Variation mode decomposition[J]. IEEE Transaction on Signal Processing,2014,62(3):531-544.
[20]夏天,程细玉. SARIMA模型的建模及其信贷预测研究[J].华侨大学学报(自然科学版),2006,27(3):332-335.
[21] LONG B,XIAN W X,LI M,et al. Improved diagnostics for the incipient faults in analog circuits using LSSVM based on PSO algorithm with Mahalanobis distance[J]. Neurocomputing,2014,133:237-248.
[22] KEERTHI S S,LIN C J. Asymptotic behaviors of support vector machines with gaussian kernel[J]. Neural Computation,2003,15(7):1667-1689.
[23] SUYKENS J A K,VANDEWALLE J. Least squares support vector machine classifiers[J]. Neural Processing Letters,1999,9(3):293-300.
[24] HU R,WEN S P,ZENG Z G,et al. A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm[J]. Neurocomputing,2017,221:24-31.
[25]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.
[26] CHEN Y H,ZHANG C A,HE K J,et al. Multi-step-ahead crude oil price forecasting using a hybrid grey wave model[J].Physica A:Statistical Mechanics and its Applications,2018,501:98-110.