Association Rules Discovery of Deviant Events in Multivariate Time Series: An Analysis and Implementation of the SAX-ARM Algorithm - Cnam - Conservatoire national des arts et métiers Access content directly
Journal Articles Image Processing On Line Year : 2022

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

Abstract

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.
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hal-04071436 , version 1 (17-04-2023)

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Axel Roques, Anne Zhao. Association Rules Discovery of Deviant Events in Multivariate Time Series: An Analysis and Implementation of the SAX-ARM Algorithm. Image Processing On Line, 2022, 12, pp.604 - 624. ⟨10.5201/ipol.2022.437⟩. ⟨hal-04071436⟩
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