Granger Causality Based Hierarchical Time Series Clustering for State Estimation

TL;DR

This paper introduces a hierarchical clustering method for multivariate time series using Granger causality and symbolic dynamic filtering, improving state estimation accuracy while reducing data dimensionality and noise.

Contribution

It presents a novel hierarchical clustering approach based on Granger causality for efficient state estimation in complex dynamical systems.

Findings

Effective noise rejection and dimensionality reduction.

Maintains high state-prediction accuracy.

Validated on occupancy and temperature datasets.

Abstract

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.