Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79425
Title: Mining spatio-temporal patterns in multivariate spatial time series
Authors: Wu, GPK 
Chan, KCC 
Keywords: Multivariate spatial time series pattern discovery
Spatio-temporal data mining
Time series clustering
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: 2018 IEEE 3rd International Conference on Big Data Analysis, ICBDA 2018, 2018, p. 50-54 How to cite?
Abstract: With the advancement of the computing technology and its wide range of applications, collecting large sets of multivariate time series in multiple geographical locations introduces a problem of identifying interesting spatio-temporal patterns. We consider a new spatial structure of the data in the pattern discovery process due to the dependent nature of the data. This paper presents an information theoretic approach to detect the temporal patterns from the multivariate time series in multiple locations. Based on their occurrences of discovered temporal patterns, we propose a method to identify interesting spatio-temporal patterns by a statistical significance test. Furthermore, the identified spatio-temporal patterns can be used for clustering and classification. For evaluating the performance, a simulated dataset is tested to validate the quality of the identified patterns and compare with other approaches. The result indicates the approach can effectively identify useful patterns to characterize the dataset for further analysis in achieving good clustering quality.
Description: 3rd IEEE International Conference on Big Data Analysis, ICBDA 2018, Shanghai, China, 9-12 March 2018
URI: http://hdl.handle.net/10397/79425
ISBN: 9781538647936
DOI: 10.1109/ICBDA.2018.8367650
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