本文選題：社會消費品零售總額 + 指數平滑方法��； 參考：《蘭州交通大學》2017年碩士論文

【摘要】：社會消費品零售總額是衡量我國人民消費水平的重要指標,也是影響國民經濟的重要因素。因此,研究我國社會消費品零售總額的發(fā)展趨勢對于我國經濟的發(fā)展具有重要的意義。社會消費品零售總額是一組時間序列時序,根據經典的時間序列預測理論,本文的具體工作包括:首先,構建了X-12-ARIMA模型(加法和乘法),同時對這兩個模型進行比較分析,結果表明X-12-ARIMA乘法模型的擬合效果高于X-12-ARIMA加法模型,乘法模型的MAPE較小,擬合程度較高。最后對模型AR、MA、ARIMA進行了比較,結果表明X-12-ARIMA乘法模型的擬合程度較高,對預測具有一定的優(yōu)勢。其次,構建了狀態(tài)空間模型下的指數平滑方法(ETS方法),對狀態(tài)空間模型下的指數平滑理論進行了系統(tǒng)的研究,并給出常用指數平滑方法的點預測推導形式。通過實證分析,最優(yōu)擬合模型為ETS(M,MD,M),結果表明ETS模型的擬合程度和預測精度都比較高,模型的MAPE較小,模型對原始時序的季節(jié)性、趨勢性和周期性因素擬合較好。ETS模型能夠充分的剔除原始時序中所包含的各項信息。再次,根據單項預測模型的擬合效果,本文構建了組合預測模型。并在此基礎上引入了兩種優(yōu)化權重系數算法,分別為非線性規(guī)劃方法和混沌粒子群優(yōu)化算法。根據實證分析,結果表明基于混沌粒子群算法權重優(yōu)化的組合模型擬合程度更高,且擬合效果均高于單項預測方法。應用混沌粒子群算法來優(yōu)化權重系數,較大程度上提高了模型的擬合精度和預測精度。組合模型充分的應用了各個單項模型的優(yōu)點,同時將單項預測模型的優(yōu)勢結合到了一起。最后,根據上述單項模型和組合模型的研究結果,進行分析比較結果表明基于混沌粒子群算法來優(yōu)化權重系數的組合模型的擬合程度較高,對我國社會消費品零售總額的擬合預測程度較好,MAPE較小。并應用本文所建立的兩類權重優(yōu)化方法的組合模型對我國社會消費品零售總額時序進行了數據的擬合和預測對比分析,同時對未來的我國社會消費品零售總額時序進行了預測。綜上所述,組合模型的擬合精度均高于單項預測模型的擬合精度,而在組合結構中,應用混沌粒子群算法對組合權重系數進行優(yōu)化能夠進一步提高組合模型的擬合精度,因此本文所建立的組合預測模型是有效的,具有一定的實用價值和指導意義。
[Abstract]:The total retail sales of consumer goods is an important index to measure the consumption level of Chinese people, and also an important factor affecting the national economy. Therefore, it is of great significance to study the development trend of retail sales of consumer goods in China. The total retail sales of consumer goods is a group of time series. According to the classical time series prediction theory, the specific work of this paper includes: firstly, the X-12-ARIMA model (addition and multiplication) is constructed, and the two models are compared and analyzed. The results show that the fitting effect of the X-12-ARIMA multiplication model is higher than that of the X-12-ARIMA addition model. The MAPE of the multiplication model is smaller and the fitting degree is higher. Finally, the comparison of the model ARGMAA and ARIMA shows that the fitting degree of the X-12-ARIMA multiplication model is high, and it has some advantages for prediction. Secondly, the exponential smoothing method under the state space model is constructed and the ETS method is constructed. The exponential smoothing theory under the state space model is systematically studied, and the point prediction derivation form of the commonly used exponential smoothing method is given. Through the empirical analysis, the optimal fitting model is ETS MMDM. The results show that the ETS model has higher fitting degree and prediction accuracy, the MAPE of the model is smaller, and the model is seasonal to the original time series. The trend and periodicity factors fit well. The ETS model can fully eliminate the information contained in the original time series. Thirdly, according to the fitting effect of the single prediction model, the combined prediction model is constructed in this paper. On this basis, two kinds of optimization weight coefficient algorithms are introduced, which are nonlinear programming method and chaotic particle swarm optimization algorithm. According to the empirical analysis, the results show that the combined model based on the weight optimization of chaotic particle swarm optimization has higher fitting degree, and the fitting effect is higher than the single prediction method. Chaotic particle swarm optimization algorithm is used to optimize the weight coefficient, which greatly improves the fitting accuracy and prediction accuracy of the model. The combined model fully utilizes the advantages of each single model and combines the advantages of the single prediction model. Finally, according to the research results of the above single model and combination model, the results of analysis and comparison show that the combination model based on chaotic particle swarm optimization has higher fitting degree. The fitting prediction of the total retail sales of consumer goods in China is better than that of MAPE. Using the combined model of the two kinds of weight optimization methods established in this paper, the time series of the total retail sales of consumer goods in China is fitted and compared, and the future time series of the total retail sales of consumer goods in China is forecasted. In conclusion, the fitting accuracy of the combined model is higher than that of the single prediction model. In the combinatorial structure, the optimization of the combined weight coefficient by using chaotic particle swarm optimization algorithm can further improve the fitting accuracy of the combined model. Therefore, the combined prediction model established in this paper is effective and has certain practical value and guiding significance.
【學位授予單位】：蘭州交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F224;F724

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