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1

I think that you miss the second parameter: min_samples=200 DBSCAN not only detects the outliers, but it mainly detects so-called noise. When we do clustering via DBSCAN, we do not look only at distance eps=0.6, but we check if the cluster-candidate is populated with over than min_samples=200 objects. You don't see "outliers", but you see all the ...


1

As a general principle for: process the sequence by splitting into smaller, overlapping analysis windows. The window length should be slightly longer than your event of interest. In such a window you can compute features that characterize the event, such a the difference between min and max inside the window. With such features the events shown here should ...


0

For me it sounds like Time Series Anomaly Detection. You can follow the example here: https://towardsdatascience.com/time-series-anomaly-detection-with-pycaret-706a6e2b2427. In Pycaret there are over 10 anomaly detection algorithms (wrapper of the pyod library). You should use several of them, play with the fraction (threshold) and check which combination ...


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A bit far fetched, though, if you are looking for fraud, maybe it would be worthwhile to check for values with strange digit patterns, like 999999000000.00, following ideas from Benford's law. Maybe this could be a score to add to your factors.


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