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-3 votes

Methods for Detecting outliers in a time series

One definition of outlier is the following: lower outliers: all points which are less than $Q1 - 1.5 \times IQR$, upper outliers: all points which are greater than $Q3 + 1.5 \times IQR$ ($Q1$ and $Q3$ ...
Sane's user avatar
  • 459
3 votes

to determine the appropriate threshold of the z-score for the non-normally distributed data

TL:DR I'm not sure this is needed, but, if it is, there will not be a simple method of doing it. Longer answer: First, your data look pretty darn normal. Formal tests of normality are lousy. The best ...
Peter Flom's user avatar
  • 124k
11 votes

Methods for Detecting outliers in a time series

An outlier is a surprising point. What points would surprise you? Make up a rule and apply it. What rule you make up depends on why you are detecting outliers in the first place. Many times, when ...
Peter Flom's user avatar
  • 124k
0 votes

Do we need to split the data for Unsupervised Anomaly Detection?

The accepted answer touches on a lot of this, but I've tried to answer your questions more explicitly here. Why should I split or not split my datasets when I apply unsupervised anomaly/outlier ...
noNameTed's user avatar
  • 153
2 votes
Accepted

Calculate the confidence that the data point is NOT explained by the regression

On the one hand, you can absolutely use prediction intervals (your software should be able to do this by itself) and check whether a data point falls outside some (say) 99% PI. Of course, this can ...
Stephan Kolassa's user avatar
0 votes
Accepted

Interpreting Mass-Volume as an evaluation criterion for unsupervised anomaly detection

Short answers, since nobody seems to have replied: You ask if "s is a good model." Being pedantic: s is not a model at all - it is a scoring function. As to what makes it good, I don't see ...
user1557414's user avatar

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