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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Vydané v:	IEEE sensors journal Ročník 23; číslo 4; s. 1
Hlavní autori:	Wei, Yuanyuan, Jang-Jaccard, Julian, Xu, Wen, Sabrina, Fariza, Camtepe, Seyit, Boulic, Mikael
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 15.02.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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