  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 dependences). 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 dependences 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 CO2
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.
