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Rewicki, Ferdinand (2024) Unraveling Anomalies in Time (Version 1.0.0). [Other]
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Official URL: https://archive.softwareheritage.org/swh:1:rev:f75c762d3c444d47469146b125e066b8330b6e5e
Abstract
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.
| Item URL in elib: | https://elib.dlr.de/207220/ | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Document Type: | Other | ||||||||
| Title: | Unraveling Anomalies in Time (Version 1.0.0) | ||||||||
| Authors: |
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| Date: | 14 June 2024 | ||||||||
| Refereed publication: | No | ||||||||
| Open Access: | No | ||||||||
| Status: | Published | ||||||||
| Keywords: | Anomaly Detection, Anomaly Clustering, Time Series, Controlled Environment Agriculture, Anomaly Discovery | ||||||||
| HGF - Research field: | Aeronautics, Space and Transport | ||||||||
| HGF - Program: | Space | ||||||||
| HGF - Program Themes: | Space System Technology | ||||||||
| DLR - Research area: | Raumfahrt | ||||||||
| DLR - Program: | R SY - Space System Technology | ||||||||
| DLR - Research theme (Project): | R - EDEN ISS Follow-on | ||||||||
| Location: | Jena | ||||||||
| Institutes and Institutions: | Institute of Data Science > Data Analysis and Intelligence | ||||||||
| Deposited By: | Rewicki, Ferdinand | ||||||||
| Deposited On: | 05 Nov 2024 15:55 | ||||||||
| Last Modified: | 02 Dec 2025 13:50 |
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