[IPOL announce] new article: Association Rules Discovery of Deviant Events in Multivariate Time Series: An Analysis and Implementation of the SAX-ARM Algorithm

[announcements about the IPOL journal]

A new article is available in IPOL: https://www.ipol.im/pub/art/2022/437/


Axel Roques, and 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, 12 (2022), pp. 604–624.
https://doi.org/10.5201/ipol.2022.437

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.