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We have been testing the core anomaly detection algorithm of Eyer by calculating F1 score, recall and precision using the a small part of the Server Machine Dataset, that is a labelled open source dataset available online. We have been testing first using the data set as it and then relabelling the setit. The relabelling was done following the observation that some event that by looking at the data seemed to be anomalous, were not labelled as such. We are going to discuss this observation in the section dedicated to relabelling. This is a test on a reduced set of data, in the future we plan to release more notes on testing with larger datasets. Our algorithm scores high in Recall for both cases, the original one and the relabelled one, this means that is good in capturing all the labelled anomalies. For the original labelling the precision and, consequently, F1 are low, but become high in the relabelled case. For the relabelled case we reach an F1 = 0.81 (0.86 if we consider only the critical anomalies) after the first 4 weeks of data collection have passed.
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