[R] Outlier Detection for timeseries

[Bert Gunter]

Danger: More careful thought required.

"Outlier....s" (Title of a TECHNOMETRICS paper of a couple of decades ago)
are an artificial construct: there is NO SUCH THING in the abstract. They
exist only wrt to a model. So there is no such thing as software that "tells
whether the changes are considered ...". Rather, you must consider
alternative "suitable" models, examine their fits, scientific implications,
interpretation, etc. Frequently, several models will fit essentially equally
well, but different subsets of the data will appear "unusual" (I no longer
use the word "outlier" because of the intimation that there is an objective
statistical meaning to this term, which there is not) for each.

Statistical algorithms cannot replace careful thinking. Sorry about that.

-- Bert Gunter
Genentech


-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of Pele
Sent: Saturday, February 14, 2009 5:16 AM
To: r-help at r-project.org
Subject: Re: [R] Outlier Detection for timeseries


Hi Stephen,

I am doing cross correlation analysis and I am trying to find a outlier
detection function in R that can detect changes in the level of the response
series that are not accounted for by the estimated model. Something that
tells whether the changes are considered Additive Outliers, Level Shifts, or
Temporary Changes... The output in the original not is what SAS produces and
I was looking for something similar..  R is very new to me (4 weeks) hence
still feeling my way around...

Many thanks!



Pele wrote:
> 
> Hello R users,
> 
> Can someone tell if there is a package in R that can do outlier detection
> that give outputs simiilar to what I got from SAS  below.
> 
> Many thanks in advance for any help!
> 
>                               Outlier Details
>

> Approx
>                                                                        
> Chi-     Prob>
>            Obs    Time ID         Type                  Estimate     
> Square     ChiSq
> 
>             12       12.000000    Additive             2792544.6     
> 186.13    <.0001
>             13       13.000000    Additive              954302.1      
> 21.23    <.0001
>             15       15.000000    Shift                    63539.3       
> 9.06    0.0026
> 
> 

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