[RsR] Outliers and ARMA?

[Dayashka Khan]

hello Ajay
can you please discuss three main points regarding your code
1) why you have fitted AR1 on the variable pi (means how you have
identified it)
2) Why you have used the unique(sort()) command when the detectAO and
detectIO already identify outliers at unique points. Even after using this
command the results have AO and IO detected at same index eg index 70 and
at 196.
3)What is the main objective of your study and how will you incorporate
these identified outliers in your analysis or whether you just want to
identify them graphically?
regards



On Tue, Oct 29, 2013 at 1:26 PM, Ajay Shah <ajayshah using mayin.org> wrote:

> When I use TSA::detectIO() and TSA::detectAO() I find that my data has a
> few outliers. What should one do next?
>
> Here's a self-contained example:
>
> pi <- c(9.11, 4.53, 20.17, 13.26, 15.29, 12.95, 14.93, 12.65, -4.2,
> -4.22, 8.42, 10.44, 8.29, 14.37, 20.24, 17.93, 11.8, 17.49, 3.85,
> 9.58, -9.58, 0, 9.58, 7.61, 7.56, 20.55, 23.84, 26.93, 7.08, 8.79,
> 6.99, 6.95, -13.93, -8.79, 5.28, 10.5, 19.01, 13.64, 18.5, 21.5,
> 14.66, 3.23, 6.44, 11.19, 3.18, 0, 0, 3.17, 11.03, -7.87, 11, 9.35,
> 3.1, 7.72, 12.25, 4.56, 19.56, 36.75, -5.8, -5.83, 8.74, 18.72, 31.02,
> 35.66, 5.39, 20.01, 36.48, 14.04, -24.35, -26.17, -14.64, -2.68, 2.68,
> 11.99, 2.65, 11.85, 5.23, 9.1, 21.81, 2.54, -19.18, 0, -2.58, 10.29,
> 11.47, 5.06, 6.3, 7.52, -5.01, 2.51, 13.69, 2.47, -11.16, -2.49,
> -5.01, 5.01, 8.71, 7.42, 15.91, 15.7, 8.37, -3.58, 8.34, 9.46, -7.09,
> -4.75, -2.38, 4.76, 2.37, 7.09, 10.55, 12.77, 6.91, 3.44, 4.57, 4.55,
> -12.57, -2.3, 2.3, 8.01, 14.74, 2.25, 6.73, 10.02, -4.44, 0, 8.87,
> 3.31, -5.52, 5.52, 0, 0, 0, 8.78, 9.8, 11.88, 10.7, 3.19, 6.35, -2.11,
> -9.56, 11.68, -2.11, 0, 9.49, -5.26, 5.26, 19.78, 4.12, 5.13, 13.25,
> 10.09, -6.05, 2.02, 0, 0, 10.04, 9.96, 19.67, 9.72, 0, 9.64, 19.05, 0,
> 0, 0, 9.41, -9.41, 9.41, 9.34, 9.27, 18.32, 9.06, 0, 8.99, 0, 0, 0,
> 8.92, 17.65, 8.73, 8.66, 8.6, 25.44, 16.67, 8.25, 16.33, 0, -8.14,
> 8.14, 0, 0, 16.11, 7.97, 15.79, 53.68, 14.91, 7.38, 14.63, 21.62,
> 7.12, 21.11, -14.04, 0, 0, 14.04, 13.87, 27.27, 0, 6.72, 13.33, 6.61,
> 19.62, 19.3, -19.3, 0, 6.47, 6.43, 12.77, 25.13, 6.2, 18.41, 6.08,
> 6.05, -12.12, 6.08, 6.05, 12, 23.65, 5.84, 11.59, 22.86, 11.27, 5.59,
> 11.11, 5.52, 5.49, 10.91, 10.81, 5.37, 10.67, 10.57, 15.69, 20.6,
> 10.17)
> timeaxis <- seq(from=as.yearmon("Apr 1993"), to=as.yearmon("Aug 2013"),
> by=1/12)
>
> m <- arima(pi, order=c(1,0,0), seasonal=list(order=c(1,0,0), period=12))
> errors <- resid(m)
> boxplot(errors)                         # There are a few outliers
>
> library(TSA)
> weird <- unique(sort(c(detectIO(m)$ind, detectAO(m)$ind)))
> par(mai=c(.4, .8, .2, .2))
> plot(timeaxis, pi, type="l", col="blue", xlab="", ylab="Per cent");
> abline(h=0)
> points(timeaxis[weird], pi[weird], col="red", cex=2)
>
> Any suggestions would be most appreciated. :)
>
> --
> Ajay Shah
> ajayshah using mayin.org
> http://www.mayin.org/ajayshah
> http://ajayshahblog.blogspot.com
>
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>
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