8/2017 UPDATE: My ML based anomales and patterns change detection tool - SETDS was redeveloped on R. See more details:
_______________________________________ original post:
I have already suggested (and partially tested) to use R to developed an exception (anomaly) detector by applying my SETDS Methodology. You can find some simple examples in my CMG.org papers or here or at the following post:
I did not used any specific statistical packages for that
(e.g. qcc), but I see now some very specific ones have been appearing that could be used to detect different type of anomalies.
Here is one at Twitter Blogs:
Introducing practical and robust anomaly detection in a time series
Not sure how the approach evaluate (score) significance of the anomaly like EV meta-metric does in my SETDS Methodology. I see at least it puts them in some categories such as "global anomalies" and "local anomalies".
I may want to test the package. You?
Igor = I go R. I have redeveloped SETDS on R = SonR
I have already suggested (and partially tested) to use R to developed an exception (anomaly) detector by applying my SETDS Methodology. You can find some simple examples in my CMG.org papers or here or at the following post:
SEDS-Lite: Using Open Source Tools (R, BIRT, MySQL) to Report and Analyze Performance Data
I did not used any specific statistical packages for that
(e.g. qcc), but I see now some very specific ones have been appearing that could be used to detect different type of anomalies.
Here is one at Twitter Blogs:
Introducing practical and robust anomaly detection in a time series
Not sure how the approach evaluate (score) significance of the anomaly like EV meta-metric does in my SETDS Methodology. I see at least it puts them in some categories such as "global anomalies" and "local anomalies".
I may want to test the package. You?
No comments:
Post a Comment