SAX-ARM: Deviant event pattern discovery from multivariate time series using symbolic aggregate approximation and association rule mining

Abstract

The discovery of event patterns from multivariate time series is important to academics and practitioners. In particular, we consider the event patterns related to anomalies such as outliers and deviations, which are important factors in system monitoring for manufacturing processes. In this paper, we propose a method for discovering the rules to describe deviant event patterns from multivariate time series, called SAX-ARM (association rule mining based on symbolic aggregate approximation). Inverse normal transformation (INT) is first adopted for converting the distribution of time series to the normal distribution. Then, symbolic aggregate approximation (SAX) is applied to symbolize time series, and association rule mining (ARM) is used for discovering frequent rules among the symbols of deviant events. The experimental results show the discovery of informative rules among deviant events in a multivariate time series from a die-casting manufacturing process that has ten variables with 1,437 lengths. We also present the results of sensitivity analysis, which demonstrates that significant rules can be discovered with different settings of the SAX parameters. The results describe the usefulness of the proposed method to identify deviant event among multivariate time series with high complexity.