# 【精品文档】73中英文双语毕业设计外文文献翻译成品：一种基于自回归整合移动平均（ARIMA）模型、灰色模型和反向传播神经网络（BPNN）模型对消费物价指数（CPI）进行预测的新型分治模型.docx

此文档是毕业设计外文翻译成品（ 含英文原文中文翻译），无需调整复杂的格式下载之后直接可用，方便快捷本文价格不贵,也就几十块钱一辈子也就一次的事 外文标题A Novel Divide-and-Conquer Model for CPI Prediction Using ARIMA, Gray Model and BPNN 外文作者Joseph Aiden，Zavier Robot 文献出处 Procedia Computer Science .2018.31842-851 如觉得年份太老，可改为近2年，毕竟很多毕业生都这样做 英文3890单词，20217字符字符就是印刷符，中文6398汉字。 A Novel Divide-and-Conquer Model for CPI Prediction Using ARIMA, Gray Model and BPNN AbstractThis paper proposes a novel divide-and-conquer model for CPI prediction with the existing compilation of the Consumer Price Index CPI in China. Historical national CPI time series is preliminary divided into eight sub-inds including food, articles for smoking and drinking, clothing, household facilities, articles and maintenance services, health care and personal articles, transportation and communication, recreation, education and culture articles and services, and residence. Three models including back propagation neural network BPNN model, grey forecasting model GM 1, 1 and autoregressive integrated moving average ARIMA model are established to predict each sub-index, respectively. Then the best predicting result among the three models’ for each sub-index is identified. To further improve the perance, special modification in predicting is done to sub-CPIs whose forecasting results are not satisfying enough. After improvement and error adjustment, we get the advanced predicting results of the sub-CPIs. Eventually, the best predicting results of each sub-index are integrated to the forecasting results of the national CPI. Empirical analysis demonstrates that the accuracy and stability of the introduced in this paper is better than many commonly adopted forecasting s, which indicates the proposed is an effective and alternative one for national CPI prediction in China. 1.Introduction 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. For instance, The People s Bank of China raised the deposit reserve ratio in January, 2008 before the CPI of 2007 was announced, for it is estimated that the CPI in 2008 will increase significantly if no action is taken. 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. Previous studies have already proposed many s and models to predict economic time series or inds such as CPI. Some previous studies make use of factors that influence the value of the index and forecast it by investigating the relationship between the data of those factors and the index. These forecasts are realized by models such as Vector autoregressive VAR model1 and genetic algorithms-support vector machine GA-SVM 2. However, these factor-based s, although effective to some extent, simply rely on the correlation between the value of the index and limited number of exogenous variables factors and basically ignore the inherent rules of the variation of the time series. As a time series itself contains significant amount of ination3, often more than a limited number of factors can do, time series-based models are often more effective in the field of prediction than factor-based models. Various time series models have been proposed to find the inherent rules of the variation in the series. Many researchers have applied different time series models to forecasting the CPI and other time series data. For example, the ARIMA model once served as a practical in predicting the CPI4. It was also applied to predict submicron particle concentrations frommeteorological factors at a busy roadside in Hangzhou, China5. What’s more, the ARIMA model was adopted to analyse the trend of pre-monsoon rainfall data forwestern India6. Besides the ARIMA model, other models such as the neural network, gray model are also widely used in the field of prediction. Hwang used the neural-network to forecast time series corresponding to ARMA p, q structures and found that the BPNNs generally per well and consistently when a particular noise level is considered during the network training7. Aiken also used a neural network to predict the level of CPI and reached a high degree of accuracy8. Apart from the neural network models, a seasonal discrete grey forecasting model for fashion retailing was proposed and was found practical for fashion retail sales forecasting with short historical data and better than other state-of-art forecastingtechniques9. Similarly, a discrete Grey Correlation Model was also used in CPI prediction10. Also, Ma et al. used gray model optimized by particle swarm optimization algorithm to forecast iron ore import and consumption of China11. Furthermore, to deal with the nonlinear condition, a modified Radial Basis Function RBF was proposed by researchers. In this paper, we propose a new called “divide-and-conquer model” for the prediction of the CPI.We divide the total CPI into eight categories according to the CPI construction and then forecast the eight sub- CPIs using the GM 1, 1 model, the ARIMA model and the BPNN. To further improve the perance, we again make prediction of the sub-CPIs whose forecasting results are not satisfying enough by adopting new forecasting s. After improvement and error adjustment, we get the advanced predicting results of the sub-CPIs. Finally we get the total CPI prediction by integrating the best forecasting results of each sub-CPI. The rest of this paper is organized as follows. In section 2, we give a brief introduction of the three models mentioned above. And then the proposed model will be demonstrated in the section 3. In section 4 we provide the forecasting results of our model and in section 5 we make special improvement by adjusting the forecasting s of sub-CPIs whose predicting results are not satisfying enough. And in section 6 we give elaborate discussion and uation of the proposed model. Finally, the conclusion is summarized in section 7. 2. Introduction to GM1,1, ARIMA 2. Articles for Smoking and Drinking CPI; 3. Clothing CPI; 4. Household Facilities, Articles and Maintenance Services CPI; 5. Health Care and Personal Articles CPI; 6. Transportation and Communication CPI; 7. Recreation, Education and Culture Articles and Services CPI; 8. Residence CPI. The weight coefficient of each sub-CPI is shown in Table 8. Table 1. 8 sub-CPIs weight coefficient in the total index Note The index number stands for the corresponding type of sub-CPI mentioned before. Other inds appearing in this paper in such have the same meaning as this one. So the decomposition ula is presented as follows where TI is the total index; Ii i 1,2, ,8 are eight sub-CPIs. To verify the ula, we substitute historical numeric CPI and sub-CPI values obtained in Step1 into the ula and find the ula is accurate. Step3 The construction of the GM 1, 1 model, the ARIMA p, d, q model and the BPNN model. The three models are established to predict the eight sub-CPIs respectively. Step4 Forecasting the eight sub-CPIs using the three models mentioned in Step3 and choosing the best forecasting result for each sub-CPI based on the errors of the data obtained from the three models. Step5 Making special improvement by adjusting the forecasting s of sub-CPIs whose predicting results are not satisfying enough and get advanced predicting results of total CPI. Step6 Integrating the best forecasting results of 8 sub-CPIs to the prediction of total CPI with the decomposition ula in Step2. In this way, the whole process of the prediction by the dividing-integration model is accomplished. 3.2. The construction of the GM1,1 model The process of GM 1, 1 model is represented in the following steps Step1 The original sequence Step2 Estimate the parameters a and u using the ordinary least square OLS. Step3 Solve equation as follows. Step4 Test the model using the variance ratio and small error possibility. The construction of the ARIMA model Firstly, ADF unit root test is used to test the stationarity of the time series. If the initial time series is not stationary, a differencing transation of the data is necessary to make it stationary. Then the values of p and q are determined by observing the autocorrelation graph, partial correlation graph and the R-squared value. After the model is built, additional judge should be done to guarantee that the residual error is white noise through hypothesis testing. Finally the model is used to forecast the future trend of the variable. The construction of the BPNN model The first thing is to decide the basic structure of BP neural network. After experiments, we consider 3 nodes and 1 output nodes to be the best for the BPNN model. This means we use the CPI data of time , ,toforecast the CPI of time . The hidden layer level and the number of hidden neurons should also be defined. Since the single-hidden- layer BPNN are very good at non-liner mapping, the model is adopted in this paper. Based on the Kolmogorov theorem and testing results, we define 5 to be the best number of hidden neurons. Thus the 3-5-1 BPNN structure is determined. As for transferring function and training algorithm, we select ‘tansig’ as the transferring function for middle layer, ‘logsig’ for layer and ‘traingd’ as training algorithm. The selection is based on the actual perance of these functions, as there are no existing standards to decide which ones are definitely better than others. Eventually, we decide the training times to be 35000 and the goal or the acceptable error to be 0.01. 4.Empirical Analysis CPI data from Jan. 2012 to Mar. 2013 are used to build the three models and the data from Apr. 2013 to Sept. 2013 are used to test the accuracy and stability of these models. What’s more, the MAPE is adopted to uate the perance of models. The MAPE is calculated by the equation Data source An appropriate empirical analysis based on the above discussion can be pered using suitably disaggregated data. We collect the monthly data of sub-CPIs from the website of National Bureau of Statistics of China http//www.stats.gov.cn/. Particularly, sub-CPI data from Jan. 2012 to Mar. 2013 are used to build the three models and the data from Apr. 2013 to Sept. 2013 are used to test the accuracy and stability of these models. Experimental results We use MATLAB to build the GM 1,1 model and the BPNN model, and Eviews 6.0 to build the ARIMA model. The relative predicting errors of sub-CPIs are shown in Table 2. Table 2.Error of Sub-CPIs of the 3 Models From the table above, we find that the perance of different models varies a lot, because the characteristic of the sub-CPIs are different. Some sub-CPIs like the Food CPI changes drastically with time while some do not have much fluctuation, like the Clothing CPI. We use different models to predict the sub- CPIs and combine them by equation 7. Where Y refers to the predicted rate of the total CPI, is the weight of the sub-CPI which has already been shown in Table 1and is the predicted value of the sub-CPI which has the minimum error among the three models mentioned above. The model chosen will be demonstrated in Table 3 Table 3.The model used to forecast After calculating, the error of the total CPI forecasting by the dividing-integration model is 0.0034. 5.Model Improvement Error Adjustment As we can see from Table 3, the prediction errors of sub-CPIs are mostly below 0.004 except for two sub- CPIs Food CPI whose error reaches 0.0059 and Tr