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Anomalyzer – An Anomaly Detection Package
- hlfw0rd 11y agoSuper cool. Now I can organize a statistical hierarchy of anomalies and assign to appropriate workers!
- 0xdeadbeefbabe 11y agoYou could also set up a statistical hierarchy of workers to blame for you anomalies.
- hlfw0rd 11y agotouché
- huac 11y agoLooking at your examples, seems like this is a changepoint package more than anomaly detection. There's a fair amount of user input here (still) - picking weights and thresholds is a nontrivial task.
- drewlanenga 11y agoFair point. If you're interested in the changepoint detection use cases, we have a package for that, too. :) https://github.com/lytics/impact https://github.com/lytics/impact
- alexforster 11y agoI'm not a statistician, which is why I really like this kind of approach. It feels "FiveThirtyEight" trustworthy to me – a weighted average of different opinions. The claim that it "dynamically adjusts the weight of each algorithm according to the amount of information it contains" is a bit hyperbolic, though. The "weight" function is explained in the code this way: "If either the "magnitude" [or] "fence" methods don't have any probability to contribute, we don't want to hear about it. If they do, we upweight them substantially." Regardless, this is unquestionably a great foundation to build into your own systems. Perhaps not as turn-key as the blog suggests, but still a wonderful contribution.
- boltzmannbrain 11y agoHas anyone played with Anomalyzer yet? I'd like to get the anomaly probability for every data instance of a CSV but it's unclear to me if this is possible. I want to run it on the Numenta Anomaly Benchmark dataset (https://github.com/numenta/NAB https://github.com/numenta/NAB) to see how it compares to other anomaly detection algorithms.