Unraveling Anomalies in Time (Version 1.0.0)

Kurzfassung

This repository contains the accompanying code to reproduce the results of the publication: Rewicki et al: Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System Telemetry. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2024, https://doi.org/10.1007/978-3-031-70378-2_13 In this work, we present a pipeline for deriving different anomaly types and identify recurring anomalous behavior from unlabeled telemetry data stemming from the EDEN ISS research greenhouse. This software contains the implementation of the proposed pipeline.