Hello all,
I have a general question about time series and I wonder if someone
could help me. I have time series data of this form:
x=c(rnorm(500,0,1),rnorm(500,5,1),rnorm(500,10,1),rnorm(500,3,1),rnorm(500,8,1),rnorm(500,4,1),rnorm(500,1,1),rnorm(500,7,1))
time=1:4000
plot(time,x)
Each "rnorm" generates a different cluster. I would like to do a
statistical test of mean difference between the first, the second and
the seventh cluster:
HO: mean(rnorm(500,0,1)) = mean(rnorm(500,5,1)) = mean(rnorm(500,1,1))
My questions are: (a) Can I simply do this by an ANOVA model on these
values or by change-point analysis (using "multiple.mean.norm" of the
package "changepoint")? (b) If I want to check for the autocorrelation
of my series (by "acf"), which data should I use: the residuals of the
ANOVA model or the actual data? The actual data will give me high
autocorrelation due to the trend, is that correct?
Thank you for your help,
Mike
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