Real-time unsupervised log event anomaly detection in public transportation
Detecting log data anomalies in real-time is useful since it makes it possible to apply logic that corrects the anomalies when they happen. This project presents a method for detecting public transportation bus event log data anomalies in realtime, without having a labeled data set. Initially, each unique bus trip is represented by the event frequencies, a representation that is not suitable for r