eTRUMiner: Mining Multivariate Temporal Rules from Heterogeneous and Incomplete Time Series
Resumo
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.
Palavras-chave:
Multivariate Temporal Association Rules, Temporal Association Rules, Heterogeneous Time Series, Multivariate Time Series, Data Mining
Referências
Agrawal, R., Imieliński, T., and Swami, A. (1993). Mining association rules between sets of items in large databases. In 1993 ACM SIGMOD International Conference on Management of Data, pages 207–216.
Amaral, T. and Sousa, E. (2019). Trier: A fast and scalable method for mining temporal exception rules. In XXXIV Simpósio Brasileiro de Banco de Dados, pages 1–12. SBC.
Das, G., Lin, K.-I., Mannila, H., Renganathan, G., and Smyth, P. (1998). Rule discovery from time series. In Proceedings of the 4th ACM KDD, KDD’98, pages 16–22.
de Oliveira, F., Costa, R., Goldschmidt, R., and Cavalcanti, M. C. (2017). Mineração de regras de associação multirrelação em grafos: Direcionando o processo de busca. In XXXII Simpósio Brasileiro de Banco de Dados, pages 270–275. SBC.
Han, J., Pei, J., and Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
Harms, S. K. and Deogun, J. S. (2004). Sequential association rule mining with time lags. Journal of Intelligent Information Systems.
He, G., Dai, L., Yu, Z., and Chen, C. L. P. (2024). Gan-based temporal association rule mining on multivariate time series data. IEEE Transactions on Knowledge and Data Engineering, 36(10):5168–5180.
Ho, V. L., Ho, N., Pedersen, T. B., and Papapetrou, P. (2025). Efficient generalized temporal pattern mining in time series using mutual information. IEEE Transactions on Knowledge and Data Engineering, 37(4):1753–1771.
Karasawa, E. and Sousa, E. (2022). Truminer: Mineração de regras temporais em bases de séries multivariadas e heterogêneas. In XXXVII Simpósio Brasileiro de Bancos de Dados, pages 403–408. SBC.
Karasawa, E. G. and Sousa, E. P. M. (2023). Mining temporal rules from heterogeneous multivariate time series. Journal of Information and Data Management, 14(2).
Lin, J., Keogh, E., Lonardi, S., and Chiu, B. (2003). A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD, DMKD ’03, pages 2–11.
Romani, L. A. S., de Avila, A. M. H., Zullo, J., Chbeir, R., Traina, C., and Traina, A. J. M. (2010). Clearminer: a new algorithm for mining association patterns on heterogeneous time series from climate data. In 2010 ACM Symposium on Applied Computing, pages 900–905.
Schlüter, T. and Conrad, S. (2011). About the analysis of time series with temporal association rule mining. In 2011 IEEE Symposium on Computational Intelligence in Data Mining, pages 325–332.
Segura-Delgado, A., Gacto, M. J., Alcalá, R., and Alcalá-Fdez, J. (2020). Temporal association rule mining: An overview considering the time variable as an integral or implied component. WIREs Data Mining and Knowledge Discovery, 10(4):e1367.
Srivastava, T., Mullick, I., and Bedi, J. (2024). Association mining based deep learning approach for financial time-series forecasting. Applied Soft Computing, 155:111469.
Zhao, Y. and Zhang, T. (2017). Discovery of temporal association rules in multivariate time series. In International Conference on Mathematics, Modelling and Simulation Technologies and Applications, 2017, Xiamen, pages 294–300.
Amaral, T. and Sousa, E. (2019). Trier: A fast and scalable method for mining temporal exception rules. In XXXIV Simpósio Brasileiro de Banco de Dados, pages 1–12. SBC.
Das, G., Lin, K.-I., Mannila, H., Renganathan, G., and Smyth, P. (1998). Rule discovery from time series. In Proceedings of the 4th ACM KDD, KDD’98, pages 16–22.
de Oliveira, F., Costa, R., Goldschmidt, R., and Cavalcanti, M. C. (2017). Mineração de regras de associação multirrelação em grafos: Direcionando o processo de busca. In XXXII Simpósio Brasileiro de Banco de Dados, pages 270–275. SBC.
Han, J., Pei, J., and Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
Harms, S. K. and Deogun, J. S. (2004). Sequential association rule mining with time lags. Journal of Intelligent Information Systems.
He, G., Dai, L., Yu, Z., and Chen, C. L. P. (2024). Gan-based temporal association rule mining on multivariate time series data. IEEE Transactions on Knowledge and Data Engineering, 36(10):5168–5180.
Ho, V. L., Ho, N., Pedersen, T. B., and Papapetrou, P. (2025). Efficient generalized temporal pattern mining in time series using mutual information. IEEE Transactions on Knowledge and Data Engineering, 37(4):1753–1771.
Karasawa, E. and Sousa, E. (2022). Truminer: Mineração de regras temporais em bases de séries multivariadas e heterogêneas. In XXXVII Simpósio Brasileiro de Bancos de Dados, pages 403–408. SBC.
Karasawa, E. G. and Sousa, E. P. M. (2023). Mining temporal rules from heterogeneous multivariate time series. Journal of Information and Data Management, 14(2).
Lin, J., Keogh, E., Lonardi, S., and Chiu, B. (2003). A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD, DMKD ’03, pages 2–11.
Romani, L. A. S., de Avila, A. M. H., Zullo, J., Chbeir, R., Traina, C., and Traina, A. J. M. (2010). Clearminer: a new algorithm for mining association patterns on heterogeneous time series from climate data. In 2010 ACM Symposium on Applied Computing, pages 900–905.
Schlüter, T. and Conrad, S. (2011). About the analysis of time series with temporal association rule mining. In 2011 IEEE Symposium on Computational Intelligence in Data Mining, pages 325–332.
Segura-Delgado, A., Gacto, M. J., Alcalá, R., and Alcalá-Fdez, J. (2020). Temporal association rule mining: An overview considering the time variable as an integral or implied component. WIREs Data Mining and Knowledge Discovery, 10(4):e1367.
Srivastava, T., Mullick, I., and Bedi, J. (2024). Association mining based deep learning approach for financial time-series forecasting. Applied Soft Computing, 155:111469.
Zhao, Y. and Zhang, T. (2017). Discovery of temporal association rules in multivariate time series. In International Conference on Mathematics, Modelling and Simulation Technologies and Applications, 2017, Xiamen, pages 294–300.
Publicado
29/09/2025
Como Citar
KARASAWA, Eliane; SOUSA, Elaine P. M..
eTRUMiner: Mining Multivariate Temporal Rules from Heterogeneous and Incomplete Time Series. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 40. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 344-356.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2025.247250.
