Granger Causality Based Hierarchical Time Series Clustering for State Estimation
This work addresses computational efficiency for state estimation in real-world systems like building management, though it is incremental as it builds on existing clustering and causality techniques.
The authors tackled the problem of high computational cost in state estimation for complex dynamical systems by proposing a hierarchical time series clustering method based on symbolic dynamic filtering and Granger causality, which reduced data dimensionality while maintaining state-prediction accuracy in occupancy detection and building temperature estimation tasks.
Clustering is an unsupervised learning technique that is useful when working with a large volume of unlabeled data. Complex dynamical systems in real life often entail data streaming from a large number of sources. Although it is desirable to use all source variables to form accurate state estimates, it is often impractical due to large computational power requirements, and sufficiently robust algorithms to handle these cases are not common. We propose a hierarchical time series clustering technique based on symbolic dynamic filtering and Granger causality, which serves as a dimensionality reduction and noise-rejection tool. Our process forms a hierarchy of variables in the multivariate time series with clustering of relevant variables at each level, thus separating out noise and less relevant variables. A new distance metric based on Granger causality is proposed and used for the time series clustering, as well as validated on empirical data sets. Experimental results from occupancy detection and building temperature estimation tasks show fidelity to the empirical data sets while maintaining state-prediction accuracy with substantially reduced data dimensionality.