一种基于差分灰狼算法的消费者信心预测指数的设计
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摘要
研究目标:在大数据和互联网经济发展的背景下,有效预测消费者信心指数(CCI)以保证相关政策的制定。研究方法:基于完全集合经验模态分解(CEEMD)-差分灰狼算法(DEGWO)-BP神经网络(BPNN),建立消费者信心指数预测模型,并运用DM检验法对该模型与对比模型的预测性能进行测试。研究发现:引入CEEMD法能够有效解决误差序列随机性强等缺陷;新提出的预测+模型较对比模型的预测精度明显提高,泛化能力有所增强,且更能够精准捕捉CCI的变化规律。研究创新:将CEEMD-DEGWO-BPNN模型应用于CCI预测中。研究价值:新提出的组合预测模型能够为CCI预测提供新方法,且有效地提高预测精度。
Research Objectives: In the context of big data and Internet economic development, the Consumer Confidence Index(CCI) is effectively predicted to ensure the formulation of relevant policies. Research Methods: In this paper, based on the complete set empirical mode decomposition(CEEMD)-differential grey wolf algorithm(DEGWO)-BP neural network(BPNN), the consumer confidence index prediction model is established, and the prediction performance of the model and the comparison model is tested by DM test. Research Findings: The introduction of CEEMD method can effectively solve the defects of strong randomness of error series; the proposed prediction model has higher prediction accuracy than the comparison model, and has strong generalization ability, which can capture the variation law of CCI. Research Innovations:The CEEMD-DEGWO-BPNN model is applied to CCI prediction. Research Value:The proposed combined prediction model can provide a new method for CCI prediction and improve prediction accuracy.
Research Objectives: In the context of big data and Internet economic development, the Consumer Confidence Index(CCI) is effectively predicted to ensure the formulation of relevant policies. Research Methods: In this paper, based on the complete set empirical mode decomposition(CEEMD)-differential grey wolf algorithm(DEGWO)-BP neural network(BPNN), the consumer confidence index prediction model is established, and the prediction performance of the model and the comparison model is tested by DM test. Research Findings: The introduction of CEEMD method can effectively solve the defects of strong randomness of error series; the proposed prediction model has higher prediction accuracy than the comparison model, and has strong generalization ability, which can capture the variation law of CCI. Research Innovations:The CEEMD-DEGWO-BPNN model is applied to CCI prediction. Research Value:The proposed combined prediction model can provide a new method for CCI prediction and improve prediction accuracy.
引文
[1] Al-eyd A., Barrell R., Davis E. P., 2009,Consumer Confidence Indices and Short-team Forecasting of Consumption [J], Manchester School, 77 (1), 96～111.
[2] Bates J. M., Granger C. W. J., 1969,The Combination of Forecasts [J],Operational Research Quarterly, 20 (4), 451～468.
[3] Brakel J. V. D., S?hler E., Daas P., Buelens B., 2017, Social Media as a Data Source for Official Statistics; the Dutch Consumer Confidence Index[J], Survey Methodology, 43 (2), 183～210.
[4] Chang P. C., Wang D. D., Zhou C. L., 2012, A Novel Model by Evolving Partially Connected Neural Network for Stock Price Trend Forecasting [J], Expert Systems with Applications, 39 (1), 611～620.
[5] Can H. G. K., Yunculer C., 2018,The Explanatory Power and the Forecast Performance of Consumer Confidence Indices for Private Consumption Growth in Turkey [J], Emerging Markets Flnance and Trade, 54 (9), 2136～2152.
[6] Kucukaslan A., Sadullah C., 2010,Women Feel More Pessimistic than Men: Empirical Evidence from Turkish Consumer Confidence Index [J], Journal of Business Economics and Management, 11 (1), 146～171.
[7] Kara Y., Boyacioglu M. A., Baykan ?. K., 2011, Predicting Direction of Stock Price Index Movement Using Artificial Neural Networks and Support Vector Machines: The Sample of the Istanbul Stock Exchange[J], Expert Systems with Applications, 38 (5), 5311～5319.
[8] Kim Y. O., Jeong D., Ko I. H., 2006,Combining Rainfall-runoff Model Outputs for Improving Ensemble Streamflow Prediction [J], Journal of Hydrologic Engineering, 11 (6), 578～588.
[9] Li Z. X., Tam V., 2017, A Comparative Study of a Recurrent Neural Network and Support Vector Machine for Predicting Price Movements of Stocks of Different Volatilites[C], IEEE Symposium Series on Computational Intelligence, Honolulu, HI.
[10] Liu H., Tian H. Q., Pan D. F., Li Y. F., 2013,Forecasting Models for Wind Speed Using Wavelet, Wavelet Packet, Time Series and Artificial Neural Networks[J], Applied Energy, 107,191～208.
[11] Mirjalili S., Mirjalili S. M., Lewis A., 2014, Grey Wolf Optimizer [J], Advances in Engineering Software, 69, 46～61.
[12] Song J. J., Wang J. Z., Lu H. Y., 2018,A Novel Combined Model Based on Advanced Optimization Algorithm for Short-Term Wind Speed Forecasting [J], Applied Energy, 215, 643～658.
[13] Salcedo-Sanz S., Pérez-Bellido M., Ortiz-García E. G., Portilla-Figueras A., Prieto L., Paredes D., 2009,Hybridizing the Fifth Generation Mesoscale Model with Artificial Neural Networks for Short-term Wind Speed Prediction [J], Renewable Energy, 34 (6), 1451～1457.
[14] Storn R., Price K., 1997,Differential Evolution A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces[J], Journal of Global Optimization, 11 (4), 341～359.
[15] Xiao L. Y., Wang J. Z., Hou R., Wu J., 2015,A Combined Model Based on Data Pre-analysis and Weight Coefficients Optimization for Electrical Load Forecasting[J], Energy, 82, 524～549.
[16]陈雪娇:《我国消费者信心指数趋势分析及预测——基于ARIMA模型》[J],《东方企业文化》2011年第10期。
[17]郭庆春、寇立群、孔令军、张小永、崔文娟、史永博:《基于BP神经网络的CCI预测模型》[J],《价值工程》2011年第19期。
[18]金星、邵珠超、王盛慧:《一种基于差分进化和灰狼算法的混合优化算法》[J],《科学技术与工程》2017年第16期。
[19]李晓玉:《消费者信心指数的理论背景与实际意义》[J],《统计教育》2006年第1期。
[20]刘伟江、李映桥:《基于网络搜索数据的消费者信心指数预测研究——以台湾地区为例》[J],《浙江学刊》2015年第2期。
[21]刘伟江、李映桥:《网络消费者信心指数和经济增长的动态相关性研究》[J],《财贸研究》2017年第5期。
[22]吴文锋、胡戈游、吴冲锋:《中国消费者信心指数的信号引导功能》[J],《系统工程理论方法应用》2004年第5期。
[23]张道德、俞林:《CPI、PMI与消费者信心指数(CCI)的关系研究及实证分析》[J],《中国物价》2009年第9期。
[24]张涛:《一季度消费者信心指数创新高三四线城市成为消费增长风口》[N],《中国商报》2018年5月30日。
[25]张晓帅、姚俭:《运用改进灰色模型预测消费者信心指数》[J],《统计与决策》2012年第19期。
[2] Bates J. M., Granger C. W. J., 1969,The Combination of Forecasts [J],Operational Research Quarterly, 20 (4), 451～468.
[3] Brakel J. V. D., S?hler E., Daas P., Buelens B., 2017, Social Media as a Data Source for Official Statistics; the Dutch Consumer Confidence Index[J], Survey Methodology, 43 (2), 183～210.
[4] Chang P. C., Wang D. D., Zhou C. L., 2012, A Novel Model by Evolving Partially Connected Neural Network for Stock Price Trend Forecasting [J], Expert Systems with Applications, 39 (1), 611～620.
[5] Can H. G. K., Yunculer C., 2018,The Explanatory Power and the Forecast Performance of Consumer Confidence Indices for Private Consumption Growth in Turkey [J], Emerging Markets Flnance and Trade, 54 (9), 2136～2152.
[6] Kucukaslan A., Sadullah C., 2010,Women Feel More Pessimistic than Men: Empirical Evidence from Turkish Consumer Confidence Index [J], Journal of Business Economics and Management, 11 (1), 146～171.
[7] Kara Y., Boyacioglu M. A., Baykan ?. K., 2011, Predicting Direction of Stock Price Index Movement Using Artificial Neural Networks and Support Vector Machines: The Sample of the Istanbul Stock Exchange[J], Expert Systems with Applications, 38 (5), 5311～5319.
[8] Kim Y. O., Jeong D., Ko I. H., 2006,Combining Rainfall-runoff Model Outputs for Improving Ensemble Streamflow Prediction [J], Journal of Hydrologic Engineering, 11 (6), 578～588.
[9] Li Z. X., Tam V., 2017, A Comparative Study of a Recurrent Neural Network and Support Vector Machine for Predicting Price Movements of Stocks of Different Volatilites[C], IEEE Symposium Series on Computational Intelligence, Honolulu, HI.
[10] Liu H., Tian H. Q., Pan D. F., Li Y. F., 2013,Forecasting Models for Wind Speed Using Wavelet, Wavelet Packet, Time Series and Artificial Neural Networks[J], Applied Energy, 107,191～208.
[11] Mirjalili S., Mirjalili S. M., Lewis A., 2014, Grey Wolf Optimizer [J], Advances in Engineering Software, 69, 46～61.
[12] Song J. J., Wang J. Z., Lu H. Y., 2018,A Novel Combined Model Based on Advanced Optimization Algorithm for Short-Term Wind Speed Forecasting [J], Applied Energy, 215, 643～658.
[13] Salcedo-Sanz S., Pérez-Bellido M., Ortiz-García E. G., Portilla-Figueras A., Prieto L., Paredes D., 2009,Hybridizing the Fifth Generation Mesoscale Model with Artificial Neural Networks for Short-term Wind Speed Prediction [J], Renewable Energy, 34 (6), 1451～1457.
[14] Storn R., Price K., 1997,Differential Evolution A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces[J], Journal of Global Optimization, 11 (4), 341～359.
[15] Xiao L. Y., Wang J. Z., Hou R., Wu J., 2015,A Combined Model Based on Data Pre-analysis and Weight Coefficients Optimization for Electrical Load Forecasting[J], Energy, 82, 524～549.
[16]陈雪娇:《我国消费者信心指数趋势分析及预测——基于ARIMA模型》[J],《东方企业文化》2011年第10期。
[17]郭庆春、寇立群、孔令军、张小永、崔文娟、史永博:《基于BP神经网络的CCI预测模型》[J],《价值工程》2011年第19期。
[18]金星、邵珠超、王盛慧:《一种基于差分进化和灰狼算法的混合优化算法》[J],《科学技术与工程》2017年第16期。
[19]李晓玉:《消费者信心指数的理论背景与实际意义》[J],《统计教育》2006年第1期。
[20]刘伟江、李映桥:《基于网络搜索数据的消费者信心指数预测研究——以台湾地区为例》[J],《浙江学刊》2015年第2期。
[21]刘伟江、李映桥:《网络消费者信心指数和经济增长的动态相关性研究》[J],《财贸研究》2017年第5期。
[22]吴文锋、胡戈游、吴冲锋:《中国消费者信心指数的信号引导功能》[J],《系统工程理论方法应用》2004年第5期。
[23]张道德、俞林:《CPI、PMI与消费者信心指数(CCI)的关系研究及实证分析》[J],《中国物价》2009年第9期。
[24]张涛:《一季度消费者信心指数创新高三四线城市成为消费增长风口》[N],《中国商报》2018年5月30日。
[25]张晓帅、姚俭:《运用改进灰色模型预测消费者信心指数》[J],《统计与决策》2012年第19期。