Mining Temporal Rules from Heterogeneous Multivariate Time Series
DOI:
https://doi.org/10.5753/jidm.2023.3232Keywords:
Multivariate Temporal Rules, Time Series, Data Pre-processing, Data MiningAbstract
This paper presents TRUMiner (Temporal RUles Miner), an algorithm to mine temporal rules from multivariate time series considering pairs of variables. It provides extended multivariate temporal rules that point the occurrence of the mined patterns in the original time series. Furthermore, TRUMiner can be used with any discretization method and deals with missing data and heterogeneous time series datasets, including different number of variables per time series and distinct number of observations per variable. We evaluated the algorithm on international trade multivariate data from several sources. Results show the relevance of extended rules and the algorithm applicability to heterogeneous time series, simplifying data integration and pre-processing.
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