Hamilton and Watts (1978) state this series is produced from
a cyclic industrial process with a period of 5.
Usage
data(Caffeine)
Format
The format is:
num [1:178] 0.429 0.443 0.451 0.455 0.44 0.433 0.423 0.412 0.411 0.426 ...
Details
The dataset are from the paper by Hamilton and Watts (1978, Table 1).
The series is used to illustrate how a multiplicative seasonal
ARMA model may be identified using the partial autocorrelations.
Chatfield (1979) argues that the inverse autocorrelations are
more effective for model identification with this example.
Source
Hamilton, David C. and Watts, Donald G. (1978).
Interpreting Partial Autocorrelation Functions of Seasonal Time Series Models.
Biometrika 65/1, 135-140.
References
Hamilton, David C. and Watts, Donald G. (1978).
Interpreting Partial Autocorrelation Functions of Seasonal Time Series Models.
Biometrika 65/1, 135-140.
Chatfield, C. (1979). Inverse Autocorrelations.
Journal of the Royal Statistical Society. Series A (General) 142/3, 363–377.
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> library(FitAR)
Loading required package: lattice
Loading required package: leaps
Loading required package: ltsa
Loading required package: bestglm
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/FitAR/Caffeine.Rd_%03d_medium.png", width=480, height=480)
> ### Name: Caffeine
> ### Title: Caffeine industrial time series
> ### Aliases: Caffeine
> ### Keywords: datasets
>
> ### ** Examples
>
> #Example 1
> sdfplot(Caffeine)
> TimeSeriesPlot(Caffeine)
> #
> #Example 2
> a<-numeric(3)
> names(a)<-c("AIC", "BIC", paste(sep="","BIC(q=", paste(sep="",c(0.85),")")))
> z<-Caffeine
> lag.max <- ceiling(length(z)/4)
> a[1]<-SelectModel(z, lag.max=lag.max, ARModel="AR", Best=1, Criterion="AIC")
> a[2]<-SelectModel(z, lag.max=lag.max, ARModel="AR", Best=1, Criterion="BIC")
> a[3]<-SelectModel(z, lag.max=lag.max, ARModel="AR", Best=1, Criterion="BICq", t=0.85)
> a
AIC BIC BIC(q=0.85)
18 11 7
>
>
>
>
>
> dev.off()
null device
1
>