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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Vydáno v:IEEE sensors journal Ročník 23; číslo 4; s. 1
Hlavní autoři: Wei, Yuanyuan, Jang-Jaccard, Julian, Xu, Wen, Sabrina, Fariza, Camtepe, Seyit, Boulic, Mikael
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 15.02.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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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Shrnutí: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 anomalies involving the observation of correlations across several data points (i.e., often referred to as long-term dependencies). We propose a hybrid deep learning model that combines LSTM with Autoencoder for anomaly detection tasks in IAQ to address this issue. In our approach, the LSTM network is comprised of multiple LSTM cells that work with each other to learn the long-term dependencies of the data in a time-series sequence. Autoencoder identifies the optimal threshold based on the reconstruction loss rates evaluated on every data across all time-series sequences. Our experimental results, based on the Dunedin CO 2 time-series dataset obtained through a real-world deployment of the schools in New Zealand, demonstrate a very high and robust accuracy rate (99.50%) that outperforms other similar models.
Bibliografie:ObjectType-Article-1
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3230361