發(fā)布時(shí)間：2018-04-30 06:12

  本文選題：SARIMA模型 + Elman模型��； 參考：《蘭州大學(xué)》2017年碩士論文

【摘要】：隨著經(jīng)濟(jì)的發(fā)展,人民生活水平得到顯著的提高,中國(guó)已成為世界上第二大經(jīng)濟(jì)體,國(guó)內(nèi)生產(chǎn)總值連年遞增,國(guó)家財(cái)政支出也呈逐年上升的趨勢(shì)。財(cái)政支出是政府實(shí)施宏觀調(diào)控,進(jìn)行資源優(yōu)化配置的有效手段。近年來,國(guó)家財(cái)政在民生方面的支出比例不斷增加,人民切實(shí)感受到了財(cái)政支出所帶來的實(shí)惠。由于財(cái)政支出數(shù)據(jù)具有明顯的季節(jié)性特征,因此在進(jìn)行數(shù)據(jù)建模時(shí),要先對(duì)原始序列進(jìn)行季節(jié)性調(diào)整。為了更好地預(yù)測(cè)財(cái)政支出的增長(zhǎng)趨勢(shì),本文首先建立了季節(jié)性差分自回歸滑動(dòng)平均模型(SARIMA)和Elman神經(jīng)網(wǎng)絡(luò)模型,結(jié)果表明SARIMA模型要優(yōu)于Elman模型。其次,又建立了 SARIMA模型和Elman模型相結(jié)合的組合模型,并對(duì)國(guó)民財(cái)政支出數(shù)據(jù)進(jìn)行最終的預(yù)測(cè)。在組合模型中,為了尋找最優(yōu)的組合系數(shù),我們采用了粒子群優(yōu)化算法(PSO),以平均相對(duì)百分比誤差(MAPE)為目標(biāo)函數(shù),使組合模型的預(yù)測(cè)誤差最小。最后,我們實(shí)證分析了 2000年1月-2015年12月財(cái)政支出序列,并對(duì)2016年的國(guó)家財(cái)政支出進(jìn)行預(yù)測(cè),結(jié)果表明組合模型的效果優(yōu)于單一模型的效果,從而為我國(guó)財(cái)政支出預(yù)算提供了科學(xué)依據(jù)。
[Abstract]:With the development of economy, the living standard of the people has been greatly improved. China has become the second largest economy in the world, the GDP has been increasing year after year, and the national fiscal expenditure is also rising year by year. Fiscal expenditure is an effective means for the government to implement macro-control and optimize the allocation of resources. In recent years, the proportion of government expenditure on people's livelihood has been increasing, and the people really feel the benefits brought by fiscal expenditure. Due to the obvious seasonal characteristics of fiscal expenditure data, the original series should be seasonally adjusted when modeling the data. In order to better predict the increasing trend of fiscal expenditure, the seasonal differential autoregressive moving average model (SARIMA) and the Elman neural network model are established in this paper. The results show that the SARIMA model is superior to the Elman model. Secondly, the combination model of SARIMA model and Elman model is established, and the final prediction of national financial expenditure data is made. In order to find the optimal combination coefficient in the combinatorial model, we adopt particle swarm optimization algorithm (PSO), with the average relative percentage error (MAPE) as the objective function, so that the prediction error of the combined model is minimized. Finally, we empirically analyze the fiscal expenditure sequence from January 2000 to December 2015, and forecast the national fiscal expenditure in 2016. The results show that the combined model is better than the single model. Thus provides the scientific basis for our country's financial expenditure budget.
【學(xué)位授予單位】：蘭州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F812.45;F224

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本文編號(hào)：1823397