[IPOL announce] new article: Association Rules Discovery of Deviant Events in Multivariate Time Series: An Analysis and Implementation of the SAX-ARM Algorithm
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Fri Dec 23 10:12:26 CET 2022
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.
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