Unsupervised TCN-AE-Based Outlier Detection for Time Series With Seasonality and Trend for Cellular Networks

Abstract

Timely identification of outliers occurring in key performance indicators (KPIs) of mobile cellular networks is crucial for prompt action to unexpected events. The KPIs of cellular networks typically exhibit seasonality and trend effects and these make the detection of outliers challenging. In this paper, an online unsupervised deep machine learning (DML) algorithm for outlier detection for time series with seasonality and trend is proposed. The proposed algorithm utilizes a neural network based on a temporal convolution network (TCN) and an autoencoder (AE) to reconstruct a time series that captures the normal behavior of the input data and then the reconstruction errors between the input and reconstructed time series at the output of the TCN-AE network are used for outlier detection. To train the TCN-AE network to learn the normal behavior of the input time series, we propose a two-step training process to overcome the presence of outliers in the training data. In addition, a novel loss function specially designed to address the seasonality and trend effects of the time series is proposed. Furthermore, a pre-processing technique is used to combat the adverse trend effect which might cause false alarms in outlier detection. The performance of the proposed TCN-AE-based outlier detection algorithm is evaluated using synthetic time series, Yahoo Webscope dataset and real time series of KPIs of mobile cellular networks. The results show that the proposed TCN-AE-based outlier detection algorithm achieves better detection accuracy in terms of F-score than other DML-based algorithms such as long short-term memory (LSTM)-AE-based algorithm and the convolutional neural network (CNN) based algorithm.