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Anomaly detection for indoor air quality (IAQ) data has become an importantarea of research as the quality of air is closely related to human health andwell-being. However, traditional statistics and shallow machine learning-basedapproaches in anomaly detection in the IAQ area could not detect anomaliesinvolving the observation of correlations across several data points (i.e.,often referred to as long-term dependences). We propose a hybrid deep learningmodel that combines LSTM with Autoencoder for anomaly detection tasks in IAQ toaddress this issue. In our approach, the LSTM network is comprised of multipleLSTM cells that work with each other to learn the long-term dependences of thedata in a time-series sequence. Autoencoder identifies the optimal thresholdbased on the reconstruction loss rates evaluated on every data across alltime-series sequences. Our experimental results, based on the Dunedin CO2time-series dataset obtained through a real-world deployment of the schools inNew Zealand, demonstrate a very high and robust accuracy rate (99.50%) thatoutperforms other similar models.

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