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Topological machine learning for multivariate time series - 2021

Topological Machine Learning For Multivariate Time Series

Research Area:  Machine Learning


We develop a method for analyzing multivariate time series using topological data analysis (TDA) methods. The proposed methodology involves converting the multivariate time series to point cloud data, calculating Wasserstein distances between the persistence diagrams and using the k-nearest neighbours algorithm (k-NN) for supervised machine learning. Two methods (symmetry-breaking and anchor points) are also introduced to enable TDA to better analyze data with heterogeneous features that are sensitive to translation, rotation or choice of coordinates. We apply our methods to room occupancy detection based on 5 time-dependent variables (temperature, humidity, light, CO2 and humidity ratio). Experimental results show that topological methods are effective in predicting room occupancy during a time window. We also apply our methods to an Activity Recognition dataset and obtained good results.


Author(s) Name:  Chengyuan Wu ORCID Icon & Carol Anne Hargreave

Journal name:  Journal of Experimental & Theoretical Artificial Intelligence

Conferrence name:  

Publisher name:  Taylor and Francis

DOI:  10.1080/0952813X.2021.1871971

Volume Information: