I have a question about automatic on-line outliers and change point detection in time series data.
Now I read the paper:
"Outliers, Level Shifts, and Variance Changes in Time Series" Ruey S.Tsay
It's written in the abstract: "..The methods employed are extremely simple yet useful.."
But for me it's not very easy to understand these methods and especially to implement.
If someone can give me a hint how to do that in R/Matlab etc I will be highly appreciated.
Best Answer
Alexandr,
You need to consider different strategies when building the model and trying to identify outliers. It is an iterative process. Let me just put out some concepts that you need to consider. Differencing, ARMA model structure, parameter changes in the model(ie CHOW test), changes in seasonality, changes in trend and TSAY's focus of outliers, level shifts and variance change. Use WLS when you find a variance change.
We have programmed it. It took us years to perfect as the TSAY work omitted other things as I have listed above.
You could use this free piece of software if you are looking just for level shifts. It does a pretty good job. http://www.beringclimate.noaa.gov/regimes/