LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time Series Data. (arXiv:2204.06701v1 [cs.LG])

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 …

anomaly detection data detection lg quality