本文選題：時(shí)間序列　切入點(diǎn)：ARIMA模型　出處：《大連海事大學(xué)》2016年碩士論文

【摘要】：時(shí)間序列分析方法是概率和數(shù)理統(tǒng)計(jì)學(xué)科應(yīng)用領(lǐng)域中的一個(gè)分支,根據(jù)數(shù)理統(tǒng)計(jì)學(xué)的基本原理,可對(duì)獲得的新數(shù)據(jù)進(jìn)行實(shí)時(shí)調(diào)整,從而確定模型的參數(shù),提高模型的預(yù)測(cè)精度。但是,由于受到各個(gè)因素的影響,它們只能對(duì)數(shù)據(jù)的整體趨勢(shì)進(jìn)行分析和預(yù)測(cè),并不能進(jìn)行全面地分析。本文是基于ARIMA模型和動(dòng)態(tài)回歸模型的組合模型研究,結(jié)合1994年到2013年的浙江省入境旅游人數(shù)數(shù)據(jù)和《基于ARIMA的組合模型問(wèn)題研究》數(shù)據(jù)進(jìn)行深入的研究,體現(xiàn)組合模型更高的預(yù)測(cè)效率。首先,深入研究了時(shí)間序列模型的相關(guān)理論和預(yù)測(cè)的相關(guān)知識(shí),透徹地分析了ARIMA模型和動(dòng)態(tài)回歸模型的建模步驟,給出了在模型建立過(guò)程中常見問(wèn)題解決方法。例如異常點(diǎn)處理辦法,對(duì)于數(shù)據(jù)是否能夠建立模型,以及建立模型的合理性給出依據(jù)。其次,對(duì)給出的數(shù)據(jù)建立ARIMA模型和動(dòng)態(tài)回歸模型,驗(yàn)證建立模型的合理性,并對(duì)數(shù)據(jù)進(jìn)行了預(yù)測(cè),根據(jù)相對(duì)百分比誤差、平均絕對(duì)誤差和平均絕對(duì)百分比誤差評(píng)價(jià)標(biāo)準(zhǔn)進(jìn)行評(píng)價(jià)。將動(dòng)態(tài)回歸模型與指數(shù)平滑模型進(jìn)行對(duì)比,體現(xiàn)出動(dòng)態(tài)回歸模型具有更高的預(yù)測(cè)效率。最后,本文選出4種組合模型的方法,進(jìn)行模型的選取工作。利用3組組合數(shù)根據(jù)SSE、MSE、MAE、MAPE和MSPE5個(gè)指標(biāo)進(jìn)行評(píng)價(jià),并與單一模型進(jìn)行了對(duì)比,證明了組合模型在模型選取方面的突破性成績(jī)。同時(shí),對(duì)4種模型進(jìn)行橫向的對(duì)比,發(fā)現(xiàn)不同組的組合模型的選取不是統(tǒng)一的,需要進(jìn)一步研究,選出最佳的組合模型。
[Abstract]:The method of time series analysis is a branch of the application field of probability and mathematical statistics. According to the basic principle of mathematical statistics, the new data can be adjusted in real time, and the parameters of the model can be determined. Improve the prediction accuracy of the model. However, because of the influence of various factors, they can only analyze and forecast the overall trend of the data. This paper is based on the combination model of ARIMA model and dynamic regression model. Based on the data of inbound tourist population of Zhejiang Province from 1994 to 2013 and the data of "combination Model based on ARIMA", this paper makes a deep study to show that the combination model has higher forecasting efficiency. The related theories of time series model and the related knowledge of prediction are deeply studied, the modeling steps of ARIMA model and dynamic regression model are thoroughly analyzed, and the solutions to common problems in the process of modeling are given, such as the method of dealing with outliers, The basis of whether the data can be established and the rationality of the model are given. Secondly, the ARIMA model and the dynamic regression model are established to verify the rationality of the model, and the data are predicted. According to the relative percentage error, average absolute error and average absolute percentage error, the evaluation criteria are evaluated. The dynamic regression model is compared with the exponential smoothing model, which shows that the dynamic regression model has higher prediction efficiency. In this paper, four methods of combinatorial models are selected to select the models. The three groups of combination numbers are used to evaluate SSEMS-MSE MAEMAE MAPE and MSPE5, and the results are compared with those of the single model. It is proved that the combination model is a breakthrough in the selection of the model. At the same time, the horizontal comparison of the four models shows that the selection of the combination model with different groups is not uniform, and needs further study to select the best combination model.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：O212.1

【相似文獻(xiàn)】

相關(guān)期刊論文 前5條

1 王泰積;劉威儀;李竹渝;;金融區(qū)間數(shù)據(jù)的動(dòng)態(tài)回歸模型比較與實(shí)證檢驗(yàn)[J];統(tǒng)計(jì)與決策;2011年06期

2 鄧興升;陳石橋;殷自成;;動(dòng)態(tài)回歸模型在變形分析中的應(yīng)用[J];大地測(cè)量與地球動(dòng)力學(xué);2011年05期

3 祝長(zhǎng)華;;FDI影響廣東三大產(chǎn)業(yè)的動(dòng)態(tài)回歸模型[J];韶關(guān)學(xué)院學(xué)報(bào);2011年06期

4 陸璇;劉慧霞;陳曉;;中國(guó)上市公司未來(lái)收益預(yù)測(cè)的實(shí)證研究——貝葉斯動(dòng)態(tài)模型及其預(yù)測(cè)[J];中南大學(xué)學(xué)報(bào)(社會(huì)科學(xué)版);2003年02期

5 ;[J];;年期

相關(guān)重要報(bào)紙文章 前1條

1 盧錚;美房市跌勢(shì)有望趨于緩和[N];中國(guó)證券報(bào);2008年

相關(guān)碩士學(xué)位論文 前1條

1 曲明輝;基于動(dòng)態(tài)回歸模型的組合模型研究[D];大連海事大學(xué);2016年

，

本文編號(hào)：1693618