1、时间序列例子yield yield plot(yield)plot(yield,type=p)plot(yield,type = o)plot(yield,type = h)plot(yield,type = o,pch=17)plot(yield,lty=2) plot(yield,lwd=2) plot(yield,main =1884-1890,xlab =年份,ylab=产量)plot(yield) abline(v=1887,lty=2) plot(yield) abline(v=c(1885,1889),lty=2) plot(yield) abline(h=c(15.5,16.5
2、),lty=2) library(TSA) data(oil.price) p88 石油价格时间序列 oil.price Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec1986 22.93 15.45 12.61 12.84 15.38 13.43 11.58 15.10 14.87 14.90 15.22 16.111987 18.65 17.75 18.30 18.68 19.44 20.07 21.34 20.31 19.53 19.86 18.85 17.271988 17.13 16.80 16.20 17.86 17.42 16.53
3、 15.50 15.52 14.54 13.77 14.14 16.381989 18.02 17.94 19.48 21.07 20.12 20.05 19.78 18.58 19.59 20.10 19.86 21.101990 22.86 22.11 20.39 18.43 18.20 16.70 18.45 27.31 33.51 36.04 32.33 27.281991 25.23 20.48 19.90 20.83 21.23 20.19 21.40 21.69 21.89 23.23 22.46 19.501992 18.79 19.01 18.92 20.23 20.98 2
4、2.38 21.77 21.34 21.88 21.68 20.34 19.411993 19.03 20.09 20.32 20.25 19.95 19.09 17.89 18.01 17.50 18.15 16.61 14.511994 15.03 14.78 14.68 16.42 17.89 19.06 19.65 18.38 17.45 17.72 18.07 17.161995 18.04 18.57 18.54 19.90 19.74 18.45 17.32 18.02 18.23 17.43 17.99 19.031996 18.85 19.09 21.33 23.50 21.
5、16 20.42 21.30 21.90 23.97 24.88 23.70 25.231997 25.13 22.18 20.97 19.70 20.82 19.26 19.66 19.95 19.80 21.32 20.19 18.33 head(oil.price)1 22.93 15.45 12.61 12.84 15.38 13.43 tail(oil.price)1 64.98 65.59 62.26 58.32 59.41 65.48 plot(oil.price)as.vector(oil.price) 1 22.93 15.45 12.61 12.84 15.38 13.43
6、 11.58 15.10 14.87 14.90 15.22 16.11 18.65 17.75 18.30 18.68 19.44 20.07 21.34 20 20.31 19.53 19.86 18.85 17.27 17.13 16.80 16.20 17.86 17.42 16.53 15.50 15.52 14.54 13.77 14.14 16.38 18.02 17.94 39 19.48 21.07 20.12 20.05 19.78 18.58 19.59 20.10 19.86 21.10 22.86 22.11 20.39 18.43 18.20 16.70 18.45
7、 27.31 33.51 58 36.04 32.33 27.28 25.23 20.48 19.90 20.83 21.23 20.19 21.40 21.69 21.89 23.23 22.46 19.50 18.79 19.01 18.92 20.23 77 20.98 22.38 21.77 21.34 21.88 21.68 20.34 19.41 19.03 20.09 20.32 20.25 19.95 19.09 17.89 18.01 17.50 18.15 16.61 96 14.51 15.03 14.78 14.68 16.42 17.89 19.06 19.65 18
8、.38 17.45 17.72 18.07 17.16 18.04 18.57 18.54 19.90 19.74 18.45115 17.32 18.02 18.23 17.43 17.99 19.03 18.85 19.09 21.33 23.50 21.16 20.42 21.30 21.90 23.97 24.88 23.70 25.23 25.13134 22.18 20.97 19.70 20.82 19.26 19.66 19.95 19.80 21.32 20.19 18.33 16.72 16.06 15.12 15.35 14.91 13.72 14.17 13.47153
9、 15.03 14.46 13.00 11.35 12.51 12.01 14.68 17.31 17.72 17.92 20.10 21.28 23.80 22.69 25.00 26.10 27.26 29.37 29.84172 25.72 28.79 31.82 29.70 31.26 33.88 33.11 34.42 28.44 29.59 29.61 27.24 27.49 28.63 27.60 26.42 27.37 26.20 22.17191 19.64 19.39 19.71 20.72 24.53 26.18 27.04 25.52 26.97 28.39 29.66
10、 28.84 26.35 29.46 32.95 35.83 33.51 28.17 28.11210 30.66 30.75 31.57 28.31 30.34 31.11 32.13 34.31 34.68 36.74 36.75 40.27 38.02 40.78 44.90 45.94 53.28 48.47 43.15229 46.84 48.15 54.19 52.98 49.83 56.35 58.99 64.98 65.59 62.26 58.32 59.41 65.48 acf(as.vector(oil.price),main=Sample ACF of the Oil P
11、rice Time Series,xaxp=c(0,24,12)acf(oil.price,main=Sample ACF of the Oil Price Time Series) pacf(as.vector(oil.price),main=Sample ACF of the Oil Price Time Series,xaxp=c(0,24,12)plot(log(oil.price)单位根检验 adf.test(log(oil.price) Augmented Dickey-Fuller Testdata: log(oil.price)Dickey-Fuller = -1.1119,
12、Lag order = 6, p-value = 0.9189alternative hypothesis: stationary plot(diff(log(oil.price)白噪声检验 for(i in 1:2)print(Box.test(diff(log(oil.price),type=Ljung-Box,lag=6*i) Box-Ljung testdata: diff(log(oil.price)X-squared = 18.5959, df = 6, p-value = 0.004903 Box-Ljung testdata: diff(log(oil.price)X-squa
13、red = 24.3938, df = 12, p-value = 0.01797acf(diff(as.vector(log(oil.price),main=Sample ACF of the Difference of the Oil Price Time Series,xaxp=c(0,24,12) pacf(diff(as.vector(log(oil.price),main=Sample PACF of the Difference of the Oil Price Time Series,xaxp=c(0,24,12) eacf(diff(log(oil.price)AR/MA 0
14、 1 2 3 4 5 6 7 8 9 10 11 12 130 x o o o o o o o o o o o o o 1 x x o o o o o o o o x o o o 2 o x o o o o o o o o o o o o 3 o x o o o o o o o o o o o o 4 o x x o o o o o o o o o o o 5 o x o x o o o o o o o o o o 6 o x o x o o o o o o o o o o 7 x x o x o o o o o o o o o o res=armasubsets(y=diff(log(oil
15、.price),nar=7,nma=7,+ y.name=test, ar.method=ols) plot(res) library(forecast) auto.arima(log(oil.price)Series: log(oil.price) ARIMA(0,1,1) Coefficients: ma1 0.2956s.e. 0.0693sigma2 estimated as 0.006717: log likelihood=260.29AIC=-516.58 AICc=-516.53 BIC=-509.62条件最小二乘估计 arima(log(oil.price),order=c(0
16、,1,1),method=CSS)Call:arima(x = log(oil.price), order = c(0, 1, 1), method = CSS)Coefficients: ma1 0.2731s.e. 0.0681sigma2 estimated as 0.006731: part log likelihood = 259.58 arima(log(oil.price),order=c(0,1,1),method=ML)极大似然估计Call:arima(x = log(oil.price), order = c(0, 1, 1), method = ML)Coefficients: ma1 0.2956s.e. 0.0693sigma2 estimated as 0.006689: log likelihood = 260.29, aic = -518.58系数的置信区间 m confint(m) 2.5 % 97.5 %ma1 0.1596829 0.4315169模型诊断 tsdiag(m) tsdiag(m) predict(m)$pred Feb2006 4.20755$se Feb2006 0.08178378
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