| 基于EEMD去噪和PSO优化的几类模型在居民消费价格指数预测中的应用研究 |
| Alternative Title | Rsearch and Application on the Consumer Price Index Forecasting Models based on EEMD and PSO
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| 张愉 |
| Thesis Advisor | 严定琪
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| 2016-05-14
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| Degree Grantor | 兰州大学
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| Place of Conferral | 兰州
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| Degree Name | 硕士
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| Keyword | 居民价格消费指数（CPI）
集合经验模态分解（EEMD）
粒子群优化算法（PSO）
BP网络
小波网络
支持向量回归机（SVR）
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| Abstract | 居民消费价格指数（CPI）经常用来衡量生活成本。 它不仅影响到政府的货币、财政、消费、物价、薪资、社会保障等政策，而且与我们的日常生活息息相关。CPI作为判断通货膨胀的主要指标，其变动受人们密切关注。因此，准确预测居民消费价格指数的变动，对CPI建立一个稳定精确的预测模型对民众、政策制定者和研究学者都有重大意义。早期对于CPI的预测研究大多采用时间序列模型，由于金融时间序列难以满足平稳性等假设，因此预测结果并不理想。后来随着人工智能模型的广泛应用，学者们将其引入到CPI的预测研究中来。在对CPI的建模预测中，噪声的影响和模型参数的选择都会影响预测的精度。为了解决这两个问题，本文以BP、小波和SVR为原始模型，提出了基于EEMD去噪和PSO优化的三种模型：EEMD-PSO-BP、EEMD-PSO-WNN和EEMD-PSO-SVR。运用三种新模型分别对1995年1月至2015年8月的CPI数据进行多步预测研究，实证结果表明EEMD算法和PSO算法在不同程度上提升了三种原始模型的性能，相比原始模型、基于EEMD去噪的模型、基于PSO优化的模型这三者，基于EEMD去噪和PSO优化的模型对居民消费价格指数预测的精度最高。根据最终的预测结果，对比得出 EEMD-PSO-SVR表现最好，该模型可应用到居民消费指数的预测研究中。 |
| Other Abstract | The Consumer Price Index (CPI) is a widely used measurement of cost of living. It not only affects the government monetary, fiscal, consumption, prices, wages, social security, but also closely relates to the residents’ daily life. As an indicator of inflation in China economy, the change of CPI undergoes intense scrutiny. Therefore, precisely forecasting the change of CPI is significant to many aspects of economics, some examples include fiscal policy, financial markets and productivity. Also, building a stable and accurate model to forecast the CPI will have great significance for the public, policymakers and research scholars. The time series model is widely used in study the CPI in the early time. However, the forecasting results are not satisfactory enough since the financial time series are usually difficult to satisfy the stability assumptions. With the widely application of the artificial intelligence model, scholars introduce it to the CPI prediction research later. In the modeling process of CPI forecasting, both the noise and the selection of model parameters will influence the accuracy of prediction. In order to solve these two problems, this article comes up with three kinds of models including EEMD-PSO-BP, EEMD-PSO-WNN and EEMD-PSO-SVR which are based on the EEMD de-noising algorithm and PSO optimization algorithm. Using three kinds of new models, respectively, to conduct multi-step prediction research on CPI data from January 1995 to August 2015, the experimental results show that EEMD algorithm and PSO algorithm can improve the performance of the original model in different degree. According to the contrast of the prediction result of three kinds of models, it is concluded that performance of the EEMD-PSO-SVR is best .This model can be applied to the prediction research of the consumer price index. |
| URL | 查看原文
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| Language | 中文
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| Document Type | 学位论文
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| Identifier | https://ir.lzu.edu.cn/handle/262010/225243
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| Collection | 数学与统计学院
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Recommended Citation
GB/T 7714 |
张愉. 基于EEMD去噪和PSO优化的几类模型在居民消费价格指数预测中的应用研究[D]. 兰州. 兰州大学,2016.
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