Anomaly Detection in Multivariate Spatial Time Series - A Ready-to-Use Implementation.

IoT technologies together with AI, and edge computing will drive the evolution of Smart Cities. IoT devices are being exponentially adopted in the urban context to implement real-time monitoring of environmental variables or city services such as air quality, parking slots, traffic lights, traffic flows, public transports etc. IoT observations are usually associated with a specific location and time slot, therefore they are spatio-temporal collections of data. And, since IoT devices are generally low-cost and low-maintenance, their data can be affected by noise and errors. For this reason, there is an urgent need for anomaly detection techniques that are able to recognize errors and noise on sensors' data streams. The Spatio-Temporal Behavioral Density-Based Clustering of Applications with Noise (ST-BDBCAN) algorithm combined with Spatio-Temporal Behavioral Outlier Factor (ST-BOF) employs both spatial and temporal dimensions to evaluate the distance between sensor observations and detect anomalies in spatial time series. In this paper, a Python implementation of ST-BOF and ST-BDBCAN in the context of IoT sensor networks is described. The implemented solution has been tested on the traffic flow data stream of the city of Modena. Four experiments with different parameters' settings are compared to highlight the versatility of the proposed implementation in detecting sensor fault and recognizing also unusual traffic conditions.