Unsupervised anomaly detection in multivariate time series with online evolving spiking neural networks

DOI10.1007/s10994-022-06129-4OpenAlexW4221006858WikidataQ114955308 ScholiaQ114955308MaRDI QIDQ2163196

Tobias Kortus, Dennis Bäßler, Gabriele Gühring

Publication date: 10 August 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-022-06129-4

zbMATH Keywords

online learningmachine learningoutlier detectionanomaly detectionevolving spiking neural networksstreaming multivariate time series

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• Cython
• Hyperopt
• LOF
• Optuna

Cites Work

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• The planar \(k\)-means problem is NP-hard
• Quantitative investigations of electrical nerve excitation treated as polarization. Translated by Nicolas Brunel and Mark C. W. van Rossum.
• A computational theory of visual receptive fields
• A practical method for outlier detection in autoregressive time series modelling
• Evolving spiking neural networks for online learning over drifting data streams
• Spiking Neuron Models
• Outlier Analysis

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