本文選題：ARIMA模型 + 預(yù)報(bào)��； 參考：《山西醫(yī)科大學(xué)》2008年碩士論文

【摘要】： 時(shí)間序列是按照時(shí)間順序取得的一系列觀察值,時(shí)間序列數(shù)據(jù)的本質(zhì)特征就是相鄰觀察值之間的依賴性。所以在對(duì)時(shí)間序列數(shù)據(jù)進(jìn)行分析時(shí),一般的回歸模型難以體現(xiàn)變量自身前后及應(yīng)變量與自變量過(guò)去的依賴關(guān)系。自回歸求和滑動(dòng)平均(ARIMA)模型是對(duì)變量自身前后依賴性進(jìn)行分析的技術(shù),它描述了變量自身當(dāng)前與過(guò)去的統(tǒng)計(jì)依賴關(guān)系,在顯示變量的動(dòng)態(tài)系統(tǒng)(dynamical system)演變規(guī)律方面有著較為豐富的結(jié)構(gòu)。 ARIMA模型由George E.P.Box和Gwilym M.Jenkins首次系統(tǒng)提出,其建模的假定較少,容易得到滿足,在現(xiàn)實(shí)的系統(tǒng)中有著廣泛的應(yīng)用。 使用ARIMA模型對(duì)時(shí)間序列進(jìn)行分析,最大的意義在于預(yù)報(bào)。本文中,我們將利用ARIMA模型對(duì)醫(yī)療資源消費(fèi)進(jìn)行預(yù)報(bào),并將預(yù)報(bào)結(jié)果與實(shí)際值和普通回歸模型的預(yù)報(bào)結(jié)果進(jìn)行比較。
[Abstract]:The time series is a series of observation values obtained according to the time sequence. The essential feature of the time series data is the dependence between the adjacent observation values. Therefore, in the analysis of time series data, the general regression model is difficult to reflect the variables before and after and the dependent relationship between dependent variables and independent variables in the past. The autoregressive summation moving average (ARIMA) model is a technique for analyzing the dependence of variables before and after, which describes the current and past statistical dependencies of variables themselves. There are abundant structures to show the evolution of dynamic system. ARIMA model is proposed by George E.P.Box and Gwilym M.Jenkins for the first time. Its modeling assumptions are few and easy to be satisfied. It is widely used in real systems. The ARIMA model is used to analyze the time series, and the greatest significance lies in the prediction. In this paper, we will use ARIMA model to forecast the consumption of medical resources, and compare the forecast results with the actual values and the results of the general regression model.
【學(xué)位授予單位】：山西醫(yī)科大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2008
【分類(lèi)號(hào)】：R311;R197.1

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 周惠;青島港貨運(yùn)吞吐量的時(shí)間序列模型[J];數(shù)理統(tǒng)計(jì)與管理;2004年04期

2 謝衷潔,王弛;用時(shí)間序列方法預(yù)測(cè)股票價(jià)格初探[J];數(shù)理統(tǒng)計(jì)與管理;2004年05期

3 湯潤(rùn)龍,顏春蓮,張曙光;紅塔系列香煙月均價(jià)格的時(shí)間序列分析[J];數(shù)理統(tǒng)計(jì)與管理;2005年01期

4 吳沖鋒,吳文鋒;基于成交量的股價(jià)序列分析[J];系統(tǒng)工程理論方法應(yīng)用;2001年01期

5 易東;張蔚;;醫(yī)院季節(jié)性時(shí)間序列資料的周期自回歸模型及其應(yīng)用[J];中國(guó)衛(wèi)生統(tǒng)計(jì);1993年05期

6 劉曉宏,金丕煥,陳啟明;ARIMA模型中時(shí)間序列平穩(wěn)性的統(tǒng)計(jì)檢驗(yàn)方法及應(yīng)用[J];中國(guó)衛(wèi)生統(tǒng)計(jì);1998年03期

，

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