时间序列的简单R函数和例子

1、4. acf计算样本自协方差函数，自相关函数以及偏相关系 数。acf(x, lag.max = NULL,type = c("correlation", "covariance", "partial"), plot = TRUE, na.action = na.fail, demean = TRUE,)5. filter生成时间序列的线性滤波filter(x, filter, method = c("convolution", "recursive"), sides = 2)recursive模

2、式是自回归的，第0步系数默认为1,yi = xi + f1*yi-1 + . + fp*yi-pThe convolution filter isyi = f1*xi+o + . + fp*xi+o-(p-1)其中o是偏移量，依赖sides的值6. lm线性回归函数计算样本自协方差函数，自相关函数以及偏相关系 数。acf(x, lag.max = NULL,type = cfcorrelation", "covariance", "partial"),plot = TRUE, na.action = na.fail, demean = TRUE

3、, .)5. filter生成时间序列的线性滤波filter(x, filter, method = cfconvolution", "recursive"), sides = 2)recursive 模式是自回归的，第0步系数默认为1,yi = xi + f1*yi-1 + fp*yi-pThe convolution filter isyi = f1*xi+o + . + fp*xi+o-(p-1)其中o是偏移量，依赖sides的值6. Im计算样本自协方差函数，自相关函数以及偏相关系 数。acf(x, lag.max = NULL,type = cfcorr

4、elation", "covariance", "partial"),plot = TRUE, na.action = na.fail, demean = TRUE, .)5. filter生成时间序列的线性滤波filter(x, filter, method = cfconvolution", "recursive"), sides = 2)recursive 模式是自回归的，第0步系数默认为1,yi = xi + f1*yi-1 + fp*yi-pThe convolution filter isyi = f1

5、*xi+o + . + fp*xi+o-(p-1)其中o是偏移量，依赖sides的值6. Im从arima模型得到模拟数据。arima.sim(n = 63, list(ar = c(0.8897,-0.4858), ma = c(-0.2279,0.2488), sd = sqrt(0.1796)10. ARMAacf ARMAtoMA acf2ARARMAac俐J用参数计算理论自相关函数或偏自相关 函数；ARMAtoMAJ用模型参数估计wold系数；acf2AR利用自相关系数计算模型自回归模型系数。ARMAacf(ar = numeric(0), ma = numeric(0), lag.

6、max = r, pacf =FALSE)ARMAtoMA(ar = numeric(0), ma = numeric(0), lag.max) acf2AR(Acf)11. ar , ar.ols , ar.yw , ar.mle利用最小二乘，yule-walker,极大似然估计方法估 计自回归模型的参数，默认利用AIC进行模型复杂度的选择。如果指定阶数，需要给定order.max的值。arima.sim(n = 63, list(ar = c(0.8897,-0.4858), ma= c(-0.2279,0.2488), sd = sqrt(0.1796)10. ARMAacf ARMAt

7、oMA acf2ARARM Aac利用参数计算理论自相关函数或偏自相关 函数；ARMAtoM利用模型参数估计wold系数；acf2AR利用自相关系数计算模型自回归模型系数。ARMAacf(ar = numeric(O), ma = numeric(O), lag.max = r, pacf =FALSE)ARMAtoMA(ar = numeric(O), ma = numeric(O), lag.max) acf2AR(Acf)11. ar , ar.ols , ar.yw , ar.mle利用最小二乘,yule-walker,极大似然估计方法估 计自回归模型的参数，默认利用AIC进行模型复杂

8、度的选择。如果指定阶数，需要给定 order.max的 值。arima.sim(n = 63, list(ar = c(0.8897,-0.4858), ma= c(-0.2279,0.2488), sd = sqrt(0.1796)10. ARMAacf ARMAtoMA acf2ARARM Aac利用参数计算理论自相关函数或偏自相关 函数；ARMAtoM利用模型参数估计wold系数；acf2AR利用自相关系数计算模型自回归模型系数。ARMAacf(ar = numeric(O), ma = numeric(O), lag.max = r, pacf =FALSE)ARMAtoMA(ar =

9、 numeric(O), ma = numeric(O), lag.max) acf2AR(Acf)11. ar , ar.ols , ar.yw , ar.mle利用最小二乘,yule-walker,极大似然估计方法估 计自回归模型的参数，默认利用AIC进行模型复杂度的选择。如果指定阶数，需要给定 order.max的 值。arima.sim(n = 63, list(ar = c(0.8897,-0.4858), ma= c(-0.2279,0.2488), sd = sqrt(0.1796)10. ARMAacf ARMAtoMA acf2ARARM Aac利用参数计算理论自相关函数或偏

10、自相关 函数；ARMAtoM利用模型参数估计wold系数；acf2AR利用自相关系数计算模型自回归模型系数。ARMAacf(ar = numeric(O), ma = numeric(O), lag.max = r, pacf =FALSE)ARMAtoMA(ar = numeric(O), ma = numeric(O), lag.max) acf2AR(Acf)11. ar , ar.ols , ar.yw , ar.mle利用最小二乘,yule-walker,极大似然估计方法估 计自回归模型的参数，默认利用AIC进行模型复杂度的选择。如果指定阶数，需要给定 order.max的 值。pa

11、r(mfrow=c(2,1) # (P)ACF of dataacf(prod, 48)pacf(prod, 48)par(mfrow=c(2,1) # (P)ACF of di dataacf(diff(prod), 48)pacf(diff(prod), 48)par(mfrow=c(2,1) # (P)ACF of d1-d12 dataacf(diff(diff(prod),12), 48)pacf(diff(diff(prod),12), 48)# # fit model (iii)prod.f让3 = arima(prod, order=c(1,1,1),seasonal=list

12、(order=c(2,1,1), period=12)prod.f让3 # to view the resultstsdiag(prod.fit3, gof.lag=48) # diagnostics# # forecasts for the final modelprod.pr = predict(prod.fit3, n.ahead=12)U = prod.pr$pred + 2*prod.pr$seL = prod.pr$pred - 2*prod.pr$semonth=337:372plot(month, prodmonth, type="o", xlim=c(33

13、7,384),ylim=c(100,180), ylab="Production")lines(prod.pr$pred, col="red", type="o")lines(U, col="blue", lty="dashed")lines(L, col="blue", lty="dashed")abline(v=372.5,lty="dotted")# # Note: This can be done using acf2.R, s

14、arima.R# # and sarima.for.R (over here ) as follows# # (remember to source the code first):prod=scan("prod.dat")acf2(prod,48)acf2(diff(prod), 48)acf2(diff(diff(prod),12), 48)# # fit model (iii)sarima(prod,1,1,1,2,1,1,12)# # forecasts for the final modelsarima.for(prod,12,1,1,1,2,1,1,12)# i

15、niai iinai iinai Iinan ! iiiibi iiiibii iiibii ainai iiiibii inaii , m m m ni n m mi nn i1 mi m iiii m iiiii mi mi w nm lain lima ! niti iExample 4.9soi = scan("soi.dat")rec = scan("recruit.dat") par(mfrow=c(2,1)soi.per = spec.pgram(soi, taper=0, log="no") abline(v=1/12

16、, lty="dotted")abline(v=1/48, lty="dotted")rec.per = spec.pgram(rec, taper=0, log="no") abline(v=1/12, lty="dotted")abline(v=1/48, lty="dotted")soi.per$spec40 # soi pgram at freq 1/12 = 40/480 soi.per$spec10 # soi pgram at freq 1/48 = 10/480 # - conf in