Anomaly Detection of Time Series Caused by International Revenue Share Fraud : Additive Model and Autoencoder Applications

Sammanfattning: In this paper, we compare the performance of two methods to find the attempts at fraud from the data provided by Sinch (formerly CLX Communications, which is a telecommunications and cloud communications platform as a service (PaaS) company). We consider the problem as finding the anomaly in a time series signal, where we ignore the duration of a single call or other features and only care about the total volume of calls in a certain period.\\ We compare Seasonal and Trend decomposition using Loess(STL) and auto-encoder-decoder under the scenario to find the anomaly in a certain period. It comes out that additive models like STL can discriminate the trending anomaly. As for auto-encoder-decoder, the anomaly can easily be found using local information, which makes the method conveniently applied. It remains a problem that unsupervised learning methods usually require manual inspection. In practical applications, we need to iterate many times with experts to find the most suitable method for that scenario.