eTRUMiner: Mining Multivariate Temporal Rules from Heterogeneous and Incomplete Time Series

This paper introduces eTRUMiner, a novel algorithm for mining multivariate temporal association rules from heterogeneous time series datasets with missing observations. Our approach enables user-defined discretization, discovers frequent patterns, and outputs rules in short or detailed formats, thus enhancing interpretability. The results show that: (i) the choice of discretization method significantly influences rule relevance; (ii) eTRUMiner preserves the rules with high confidence in a dataset with up to 15% missing data; and (iii) the extracted rules can capture plausible causal dynamics. These findings demonstrate eTRUMiner’s robustness to incomplete data and its usefulness for exploratory analysis and forecasting in complex temporal domains.