Abstract
For secure and efficient operation of engineering systems, it is of great importance to watch daily logs generated by them, which mainly consist of multivariate time-series obtained with many sensors. This work focuses on challenges in practical analyses of those sensor data: temporal unevenness and sparseness. To handle the unevenly and sparsely spaced multivariate time-series, this work presents a novel method, which roughly models temporal information that still remains in the data. The proposed model is a mixture model with dynamic hierarchical structure that considers dependency between temporally close batches of observations, instead of every single observation. We conducted experiments with synthetic and real dataset, and confirmed validity of the proposed model quantitatively and qualitatively.
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Takeishi, N., Yairi, T., Nishimura, N., Nakajima, Y., Takata, N. (2016). Dynamic Grouped Mixture Models for Intermittent Multivariate Sensor Data. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J., Wang, R. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9652. Springer, Cham. https://doi.org/10.1007/978-3-319-31750-2_18
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DOI: https://doi.org/10.1007/978-3-319-31750-2_18
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