Full paper title: Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications
Description: Anomaly detection for web applications is a challenging but important use of resources. Complicating the matter further, a lot of training data is either unlabeled or missing examples of the anomalies they're trying to prevent. This paper looks to push the state of the art forward by using a few new techniques together against seasonal KPIs.
Bio: Neil works at Pylon ai as an Analytics Engineer (a term which here means interactor with logs and metrics). He aspires to one day use some of the concepts presented here; similar techniques would also be acceptable.