An Autocorrelation-based LSTM-Autoencoder for Anomaly Detection on Time-Series Data

Data quality significantly impacts the results of data analytics. Researchers have proposed machine learning based anomaly detection techniques to identify incorrect data. Existing approaches fail to (1) identify the underlying domain constraints violated by the anomalous data, and (2) generate expl...

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Veröffentlicht in:	2020 IEEE International Conference on Big Data (Big Data) S. 5068 - 5077
Hauptverfasser:	Homayouni, Hajar, Ghosh, Sudipto, Ray, Indrakshi, Gondalia, Shlok, Duggan, Jerry, Kahn, Michael G.
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 10.12.2020
Schlagworte:	Anomaly detection Autocorrelation Big Data Data quality tests Decision trees Explainability LSTM-Autoencoder Manuals NASA Random forests Servers Time series
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