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Activity Number: 192 - Contributed Poster Presentations: Section on Statistics and the Environment
Type: Contributed
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #333062
Title: Depth-Based Clustering for Multivariate Time Series with Applications in Wind Energy
Author(s): Laura L. Tupper*
Companies: Williams College
Keywords: spatio-temporal data; multivariate time series; clustering; depth statistics; similarity measures

We explore the unsupervised classification of spatio-temporal data, with an application in wind power generation. Clustering can be used as a data-driven way to characterize the behavior of a dataset, revealing patterns and allowing the selection of representatives to characterize the space, but it relies on the selection of a useful measure of similarity between observations. Previous work has used the band distance, a depth-based distance metric for high-dimensional vector data, to cluster time series of wind speeds. We now extend the depth-based approach to take into account wind speeds at multiple locations, and compare the results of simple concatenation, dimension reduction of the multivariate data using spatial techniques, and a similarity measure based on the full matrix of times and locations.

Authors who are presenting talks have a * after their name.

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