Association Rules Discovery of Deviant Events in Multivariate Time Series: An Analysis and Implementation of the SAX-ARM Algorithm

• Published in: Image processing on line Vol. 12; pp. 604 - 624
• Main Authors: Roques, Axel, Zhao, Anne
• Format: Journal Article
• Language: English
• Published: IPOL - Image Processing on Line 23.12.2022
• Subjects: Computer Science; Apriori algorithm; deviant event discovery; Symbolic Aggregate approXimation (SAX); multivariate time series; association rule mining deviant event discovery multivariate time series Apriori algorithm Symbolic Aggregate approXimation (SAX); association rule mining
• ISSN: 2105-1232; 2105-1232
• DOI: 10.5201/ipol.2022.437

In this work, we propose an open-source Python implementation of the SAX-ARM algorithm introduced by Park and Jung (2019). This algorithm mines association rules efficiently among the deviant events of multivariate time series. To do so, the algorithm combines two existing methods, namely the Symbolic Aggregate approXimation (SAX) from Lin et al. (2003)-a symbolic representation of time series-and the Apriori algorithm from Agrawal et al. (1996)-a data mining method which outputs all frequent itemsets and association rules from a transactional dataset. A detailed description of the underlying principles is given along with their numerical implementation. The choice of relevant parameters is thoroughly discussed and evaluated using a public dataset on the topic of temperature and energy consumption. Source Code The reviewed source code and documentation for this algorithm are available from the web page of this article 1. Usage instructions are included in the archive.