本文選題：社會(huì)消費(fèi)品零售總額 + 指數(shù)平滑方法�。� 參考：《蘭州交通大學(xué)》2017年碩士論文

【摘要】：社會(huì)消費(fèi)品零售總額是衡量我國人民消費(fèi)水平的重要指標(biāo),也是影響國民經(jīng)濟(jì)的重要因素。因此,研究我國社會(huì)消費(fèi)品零售總額的發(fā)展趨勢對于我國經(jīng)濟(jì)的發(fā)展具有重要的意義。社會(huì)消費(fèi)品零售總額是一組時(shí)間序列時(shí)序,根據(jù)經(jīng)典的時(shí)間序列預(yù)測理論,本文的具體工作包括:首先,構(gòu)建了X-12-ARIMA模型(加法和乘法),同時(shí)對這兩個(gè)模型進(jìn)行比較分析,結(jié)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(jìn)行了比較,結(jié)果表明X-12-ARIMA乘法模型的擬合程度較高,對預(yù)測具有一定的優(yōu)勢。其次,構(gòu)建了狀態(tài)空間模型下的指數(shù)平滑方法(ETS方法),對狀態(tài)空間模型下的指數(shù)平滑理論進(jìn)行了系統(tǒng)的研究,并給出常用指數(shù)平滑方法的點(diǎn)預(yù)測推導(dǎo)形式。通過實(shí)證分析,最優(yōu)擬合模型為ETS(M,MD,M),結(jié)果表明ETS模型的擬合程度和預(yù)測精度都比較高,模型的MAPE較小,模型對原始時(shí)序的季節(jié)性、趨勢性和周期性因素?cái)M合較好。ETS模型能夠充分的剔除原始時(shí)序中所包含的各項(xiàng)信息。再次,根據(jù)單項(xiàng)預(yù)測模型的擬合效果,本文構(gòu)建了組合預(yù)測模型。并在此基礎(chǔ)上引入了兩種優(yōu)化權(quán)重系數(shù)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(jù)實(shí)證分析,結(jié)果表明基于混沌粒子群算法權(quán)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(xiàng)預(yù)測方法。應(yīng)用混沌粒子群算法來優(yōu)化權(quán)重系數(shù),較大程度上提高了模型的擬合精度和預(yù)測精度。組合模型充分的應(yīng)用了各個(gè)單項(xiàng)模型的優(yōu)點(diǎn),同時(shí)將單項(xiàng)預(yù)測模型的優(yōu)勢結(jié)合到了一起。最后,根據(jù)上述單項(xiàng)模型和組合模型的研究結(jié)果,進(jìn)行分析比較結(jié)果表明基于混沌粒子群算法來優(yōu)化權(quán)重系數(shù)的組合模型的擬合程度較高,對我國社會(huì)消費(fèi)品零售總額的擬合預(yù)測程度較好,MAPE較小。并應(yīng)用本文所建立的兩類權(quán)重優(yōu)化方法的組合模型對我國社會(huì)消費(fèi)品零售總額時(shí)序進(jìn)行了數(shù)據(jù)的擬合和預(yù)測對比分析,同時(shí)對未來的我國社會(huì)消費(fèi)品零售總額時(shí)序進(jìn)行了預(yù)測。綜上所述,組合模型的擬合精度均高于單項(xiàng)預(yù)測模型的擬合精度,而在組合結(jié)構(gòu)中,應(yīng)用混沌粒子群算法對組合權(quán)重系數(shù)進(jìn)行優(yōu)化能夠進(jìn)一步提高組合模型的擬合精度,因此本文所建立的組合預(yù)測模型是有效的,具有一定的實(shí)用價(jià)值和指導(dǎo)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(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F224;F724

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