PSOM: Periodic Self-Organizing Maps for Unsupervised Anomaly Detection in Periodic Time Series

Nowadays, systems providing user-oriented services often demonstrate periodic patterns due to the repetitive behaviors from people's daily routines. The monitoring data of such systems are time series of observations that record observed system status at sampled times during each day. The periodic feature and multidimensional character of such monitoring data can be well utilized by anomaly detection algorithms to enhance their detection capability. The data periodicity can be used to provide proactive anomaly prediction capability and the correlation among multidimensional series can provide more accurate results than processing the observations separately. However, existing anomaly detection methods only handle one dimensional series and do not consider the data periodicity. In addition, they often require sufficient labelled data to train the models before they can be used. In this paper, we present an unsupervised anomaly detection algorithm called Periodic Self-Organizing Maps (PSOM) to detect anomalies in periodic time series. PSOMs can be used to detect anomalies in multidimensional periodic series as well as one dimensional periodic series and aperiodic series. Our real data evaluation shows that the PSOM outperforms other supervised methods such as SARIMA and Holt-Winters method.