Fast POI anomaly detection using a weakly-supervised temporal state regression network

Point-of-interest (POI) is a fundamental data type of maps. Anomalous POIs would make maps outdated and lead to user-unfriendly location-based services, and thus should be discovered as fast as possible. Traditional POI anomaly detection methods are inefficient owing to high investigation costs. The emergence of massive human activity data provides a new insight into monitoring POI states through time series modeling. When a POI turns into an anomaly, the associated human activity would disappear. However, human activity data have complicated temporal patterns and noises. It is challenging for existing time series methods to model human activity dynamics. More importantly, there is a lag between the time a POI becomes anomalous and the time we discover it. In this research, we develop a temporal state regression network (TSRNet) model for fast POI anomaly detection. The model can extract temporal features in human activity data, and predict POI state scores as anomaly indicators. Meanwhile, an inference approach is proposed to generate state score sequences as inexact labels for model training. Such weak labels enable TSRNet to identify abnormal temporal patterns as soon as they appear, so that POI outliers can be detected at an early time. Experiments on real-word datasets from AMAP validate the feasibility of our method.