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Does Darts provide methods for unsupervised anomaly detection models? #2355
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Hi @ETTAN93, At the moment, Darts does not offer any unsupervised models for anomaly detection but it can be added to the roadmap, especially if contributors propose architectures and open PRs. |
@madtoinou thanks for that. Another thing to clarify, I used eval_accuracy in darts as part of the quantile detector class and compared it to the results I got from sklearn's recall_score. I passed the same y_test and y_pred series to both:
For some reason, I am getting the inverse of values from both, i.e. when I sum the two recall scores, I end up with 1.0. In this particular case, qd_recall from darts returns me 0.9946808510638298 whereas the recall_score from sklearn returns me 0.005319148936170213. Am I passing in the wrong parameters to the darts function? As far as I understand, the anomaly_score parameter should be the y_pred_series from the model? what does the window parameter do? The same also happens when I evaluate the accuracy score. The two scores that are returned sums up to 1.
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Hi @ETTAN93, The following should work:
outputs: You could also use Note also that in 1-2 weeks we'll release the new Darts version with the refactored anomaly detection module (including an example notebook). So the API will change slightly (see the changes and PR here). |
@dennisbader how is the |
It can be any numeric non-binary input series. The detector converts non-binary to binary.
gives |
Based on the darts documentation on anomaly models, it seems like the 2 available ones - filtering anomaly model and forecasting anomaly model both require the model to be initially fitted to a series without anomalies, i.e. a supervised anomaly detection model.
Is my understanding correct? Does Darts offer any unsupervised models for anomaly detection?
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