An Anomaly Detection Method for Multiple Time Series Based on Similarity Measurement and Louvain Algorithm

Anomaly detection is an important task for data mining. Detecting anomalies in the data collected from Municipal Solid Waste (MSW) incineration process is critical for their post processing, which helps to decrease emissions of typical flue gas pollutants and reduce costs. In this paper, we propose an unsupervised multiple time series anomaly detection model based on similarity measurement and Louvain algorithm, which consists of two stages. …