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 (ML)-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-ter...