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Each data set that is handled in the workflow is handled independently. In certain cases, it could be that you have multiple test time series available for one train time series. This can currently be implemented, by passing for each test set a data loader to the Workflow, which loads the same train data but different test data. The problem with this is that the anomaly detectors will be trained for each test set independently. If the train set is large, this could take a huge amount of time. However, for each test set, the same anomaly detector will be implemented. It would be interesting to be able to share this detector for multiple tests sets.
The text was updated successfully, but these errors were encountered:
Each data set that is handled in the workflow is handled independently. In certain cases, it could be that you have multiple test time series available for one train time series. This can currently be implemented, by passing for each test set a data loader to the Workflow, which loads the same train data but different test data. The problem with this is that the anomaly detectors will be trained for each test set independently. If the train set is large, this could take a huge amount of time. However, for each test set, the same anomaly detector will be implemented. It would be interesting to be able to share this detector for multiple tests sets.
The text was updated successfully, but these errors were encountered: