LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time Series Data

Anomaly detection for indoor air quality (IAQ) data has become an important area of research as the quality of air is closely related to human health and well-being. However, traditional statistics and shallow machine learning-based approaches in anomaly detection in the IAQ area could not detect an...

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Bibliographic Details
Published in:	IEEE sensors journal Vol. 23; no. 4; p. 1
Main Authors:	Wei, Yuanyuan, Jang-Jaccard, Julian, Xu, Wen, Sabrina, Fariza, Camtepe, Seyit, Boulic, Mikael
Format:	Journal Article
Language:	English
Published:	New York IEEE 15.02.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Air quality Anomalies Anomaly detection Atmospheric modeling Autoencoder Carbon dioxide CO<sub xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">2 Data points Deep learning Indoor air pollution Indoor air quality Logic gates Long short term memory Long short-term memory (LSTM) Machine learning Predictive models Sensors Sequences Temperature measurement Time series
ISSN:	1530-437X, 1558-1748
Online Access:	Get full text
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