# Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System Telemetry

arXiv: https://arxiv.org/abs/2406.09825 · HTML: https://mlsanity.com/paper/2406.09825/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 14 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [Benchmarking Multivariate Time Series Classification Algorithms](https://mlsanity.com/paper/2007.13156/) (2020)
- [Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection](https://mlsanity.com/paper/1804.07091/) (2018)
- [Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types](https://mlsanity.com/paper/2112.11573/) (2021)
- [Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series](https://mlsanity.com/paper/2212.11080/) (2022)
- [Autoencoder-based Anomaly Detection in Smart Farming Ecosystem](https://mlsanity.com/paper/2111.00099/) (2021)
- [A simple method for measuring inequality](https://mlsanity.com/paper/2112.15284/) (2021)

## Built on by (most cited first, 1 of 1)

- [Anomalous Agreement: How to find the Ideal Number of Anomaly Classes in Correlated, Multivariate Time Series Data](https://mlsanity.com/paper/2501.07172/) (2025)

## Abstract (arXiv)

The detection of abnormal or critical system states is essential in condition monitoring. While much attention is given to promptly identifying anomalies, a retrospective analysis of these anomalies can significantly enhance our comprehension of the underlying causes of observed undesired behavior. This aspect becomes particularly critical when the monitored system is deployed in a vital environment. In this study, we delve into anomalies within the domain of Bio-Regenerative Life Support Systems (BLSS) for space exploration and analyze anomalies found in telemetry data stemming from the EDEN ISS space greenhouse in Antarctica. We employ time series clustering on anomaly detection results to categorize various types of anomalies in both uni- and multivariate settings. We then assess the effectiveness of these methods in identifying systematic anomalous behavior. Additionally, we illustrate that the anomaly detection methods MDI and DAMP produce complementary results, as previously indicated by research.