時(shí)間序列分析r語(yǔ)言程序(參考版)
2025-06-28 07:51本頁(yè)面
【正文】 l39。l39。l39。l39。l39。LjungBox39。LjungBox39。LjungBox39。LjungBox39。LjungBox39。o39。o39。o39。LjungBox39。LjungBox39。LjungBox39。o39。o39。CSS39。o39。o39。blue39。red39。o39。o39。LjungBox39。LjungBox39。LjungBox39。l39。l39。LjungBox39。LjungBox39。LjungBox39。o39。o39。Ljung39。l39。l39。o39。o39。o39。n39。o39。o39。o39。l39。l39。b39。o39。o39。o39。b39。l39。p39。b39。mean39。o39。l39。o39。o39。o39。x39。o39。l39。)acf(x)pacf(x)錯(cuò)誤于x[i] = rnorm[i] 4/5 * rnorm[i 1] + 16/25 * rnorm[i 2] : 更換參數(shù)長(zhǎng)度為零 根據(jù)書上64頁(yè)來(lái)判斷（1，1）模型，x(t)(t1)=u(t)*(u1)，并直觀觀察該模型自相關(guān)系數(shù)和偏自相關(guān)系數(shù)的拖尾性。)acf(x)pacf(x)法二: x=rep(0:1000)for(i in 1:1000){x[i]=rnorm[i]4/5*rnorm[i1]+16/25*rnorm[i2]}plot(x,type=39。的對(duì)象不可以取子集法一：x=(n=1000,list(ma=c(4/5,16/25)))(x,type=39。)acf(x)pacf(x)錯(cuò)誤于rnorm[i] : 類別為39。)acf(x)pacf(x)法二x=rep(0:1000)for(i in 1:1000){x[i]=rnorm[i]*rnorm[i1]}plot(x,type=39。)acf(x)pacf(x)法一：x=(n=1000,list(ma=))(x,type=39。)acf(x)pacf(x)法二x=rep(0:1000)for(i in 1:1000){x[i]=rnorm[i]2*rnorm[i1]}plot(x,type=39。,xlim=c(1950,2010),ylim=c(60,100))=[,2]=acf()白噪聲檢驗(yàn)(,type=LjungBox,lag=6)(,type=LjungBox,lag=12)acf()pacf()根據(jù)自相關(guān)系數(shù)圖和偏自相關(guān)系數(shù)圖可以判斷為AR（1）模型 P81 口徑的求法在文檔上 P83arima(,order=c(1,0,0),method=ML)極大似然估計(jì)ar1=arima(,order=c(1,0,0),method=ML)summary(ar1)ev=ar1$residualsacf(ev)pacf(ev)參數(shù)的顯著性檢驗(yàn)t1=p1=pt(t1,df=48,=F)*2ar1的顯著性檢驗(yàn)t2=p2=pt(t2,df=48,=F)*2殘差白噪聲檢驗(yàn)(ev,type=LjungBox,lag=6,fitdf=1)(ev,type=LjungBox,lag=12,fitdf=1)predict(arima(,order=c(1,0,0)),=5)=predict(arima(,order=c(1,0,0)),=5)U=$pred+*$seL=$*$seplot(c(,$pred),type=l,col=1:2)lines(U,col=blue,lty=dashed)lines(L,col=blue,lty=dashed) ((n=100,list(ar=)))方法二x0=runif(1)x=rep(0,1500)x[1]=*x0+rnorm(1)for(i in 2:length(x)){x[i]=*x[i1]+rnorm(1)}plot(x[1:100],type=l)acf(x)pacf(x)擬合圖沒(méi)
公司管理相關(guān)推薦
鄂ICP備17016276號(hào)-1