Harnessing Feature Clustering For Enhanced Anomaly Detection With Variational Autoencoder And Dynamic Threshold

Thumbnail Image

Files

Harnessing_Feature_Clustering_For_Enhanced_Anomaly_Detection_With_Variational_Autoencoder_And_Dynamic_Threshold.pdf (1.85 MB)

Links to Files

https://ieeexplore.ieee.org/abstract/document/10640794

Permanent Link

https://doi.org/10.1109/IGARSS53475.2024.10640794
 http://hdl.handle.net/11603/37111

Collections

UMBC Information Systems Department
UMBC Faculty Collection
UMBC Student Collection

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0003-0130-6135

Date

2024-09-05

Type of Work

conference papers and proceedings
Text

Department

Program

Citation of Original Publication

Ale, Tolulope, Vandana P. Janeja, and Nicole-Jeanne Schlegel. “Harnessing Feature Clustering For Enhanced Anomaly Detection With Variational Autoencoder And Dynamic Threshold.” IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, July 2024, 8692–96. https://doi.org/10.1109/IGARSS53475.2024.10640794.

Rights

This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain

Subjects

Prevention and mitigation
Heuristic algorithms
Feature extraction
Anomaly Detection
Instruments
Dynamic Threshold
Green products
Variational Autoencoder
UMBC Cybersecurity Institute
Snow
Climate Extreme
Multivariate Time Series
Time series analysis

Abstract

We introduce an anomaly detection method for multivariate time series data with the aim of identifying critical periods and features influencing extreme climate events like snowmelt in the Arctic. This method leverages Variational Autoencoder (VAE) integrated with dynamic thresholding and correlationbased feature clustering. This framework enhances the VAE’s ability to identify localized dependencies and learn the temporal relationships in climate data, thereby improving the detection of anomalies as demonstrated by its higher F1-score on benchmark datasets. The study’s main contributions include the development of a robust anomaly detection method, improving feature representation within VAEs through clustering, and creating a dynamic threshold algorithm for localized anomaly detection. This method offers explainability of climate anomalies across different regions.