Caffeine: Caffeine industrial time series

In FitAR: Subset AR Model Fitting

Description

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.

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

FitAR documentation built on May 2, 2019, 3:22 a.m.