Anomaly Noise Filtering with Logistic Regression and a New Method for Time Series Trend Computation for Monitoring Systems

Abstract

Anomaly detection has always been a hot topic in signal processing and machine learning. Convolutional Neural Network (CNN) is an effective technique to detect anomaly. However, at Ant Financial, a simple CNN neglects certain patterns in real-world data that may lead to triggering of false alarms. To reduce the possibility of a false alarm, we run an anomaly noise filtering model after the CNN. In this paper, we introduce techniques to develop the model and a new method of time series trend computation. The model helps increase the accuracy in detecting false anomalies of a rise-fall pattern in the traffic(y-value) of a time series dataset. At the end of the paper, we will present the benchmarks of using our method on real online systems at Ant Financial.