Detection of Outlier Patterns in Call Records Based on Skeleton Points

With the popular use of the mobile phones, huge amount of call records are collected. The information and knowledge derived from these call records can help provide better, customized services for urban planning, public transport design, traffic engineering, disease outbreak control, and so on. In this paper, we present an outlier detection algorithm based on patterns which are formed from skeleton points of time series. The call records are massive and update rapidly in the telecommunication time series. Therefore we propose the pattern descriptor on time series frames functions which not only help to obtain a compact representation of the data streams but also to capture the main characteristics of the shape of the time series. What is more important, based on our proposed pattern descriptor, we further proposed an outlier detection algorithm which can be efficiently and effectively detect outlier patterns in the telecommunication network. Experiments on synthetic dataset and real-world call data show promising results.