[RsR] Time-series outlier problem
Victor Yohai
v|ctoryoh@| @end|ng |rom gm@||@com
Mon Jun 14 21:58:17 CEST 2021
Dear Ajay
A robust way to find outliers in a time series is using the function
arima.rob of the R package robustarima. Details on how this function is
used can be found in its help. If you want to know more about the procedure
used for this function you can consult Chapter 8 of Robust Statistics:
Theory and methods (with R) of Maronna, Martin, Salibian-Barrera and Yohai.
Víctor Yohai
-----Mensaje original-----
De: R-SIG-Robust [mailto:r-sig-robust-bounces using r-project.org] En nombre de
Ajay Shah
Enviado el: viernes, 11 de junio de 2021 6:56
Para: r-sig-robust using r-project.org
Asunto: [RsR] Time-series outlier problem
We've been using forecast::tsoutliers. In many situations it's nice, but I
have a MWE where it seems to get confused:
library(forecast)
x <- sin(seq(0,6.28,0.01)) # There's no noise
tsoutliers(ts(x)) # nothing is amiss.
x[4] <- 2
tsoutliers(ts(x)) ## Weird
x[4] <- 100
tsoutliers(ts(x)) ## Weird
It shows a lot of outliers and offers bizarre replacements.
We are working with time-series of about N=100 with gentle time series
structure. Do folks here have suggestions on how best to handle outlier
detection and correction for this?
--
Ajay Shah
ajayshah using mayin.org
http://www.mayin.org/ajayshah
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