Hello guys, I've been playing around with the OPF past days and I'm trying to optimize the anomaly scores for my data stream. I have read through the code and wiki, but I still can't figure out properly how this all works. I am looking for Temporal Anomaly and my data stream is just two columns as in the hotgym example, one with hourly timestamp and the other containing values.
1. If I put on swarming, for example by scripts/run_swarm.py with very basic .json defining it - without any custom metrics - what are the anomalies I get? Those are the raw values - the fraction of activated vs. predicted values, right? 2. Now, if I want to try the different types of anomalies, where should I implement the call in my code? Is it enough to write it into the model_params.py information and all the following calculations would be implemented by the CLAmodel? How does this affect the swarming? 3. How does metric used affect the anomaly scores? As I understand it, in no direct way, right? Thank you very much for clearing things up a bit for me! Hope I haven't missed some basic tutorial with all the explanations, though. Have a nice day, Karin -- datapine GmbH Skalitzer Straße 33 10999 Berlin email: [email protected]
