Shape-Sphere: A metric space for analysing time series by their shape
| Autor: | Eduardo Velloso, Lars Kulik, Christopher Leckie, James C. Bezdek, Masud Moshtaghi, Yousef Kowsar |
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| Rok vydání: | 2022 |
| Předmět: |
Information Systems and Management
Series (mathematics) Computer science Nearest centroid classifier k-means clustering Centroid Computer Science Applications Theoretical Computer Science Metric space Artificial Intelligence Control and Systems Engineering Feature (machine learning) Cluster analysis Algorithm Software ComputingMethodologies_COMPUTERGRAPHICS Vector space |
| Zdroj: | Information Sciences. 582:198-214 |
| ISSN: | 0020-0255 |
| DOI: | 10.1016/j.ins.2021.08.101 |
| Popis: | Shape analogy is a key technique in analyzing time series . That is, time series are compared by how much they look alike. This concept has been applied for many years in geometry. Notably, none of the current techniques describe a time series as a geometric curve that is expressed by its relative location and form in space. To fill this gap, we introduce Shape-Sphere , a vector space where time series are presented as points on the surface of a sphere. We prove a pseudo-metric property for distances in Shape-Sphere. We show how to describe the average shape of a time series set using the pseudo-metric property of Shape-Sphere by deriving a centroid from the set. We demonstrate the effectiveness of the pseudo-metric property and its centroid in capturing the ‘ shape ’ of a time series set, using two important machine learning techniques , namely: Nearest Centroid Classifier and K-Means clustering, using 85 publicly available data sets. Shape-Sphere improves the nearest centroid classification results when the shape is the differentiating feature while keeping the quality of clustering equivalent to current state-of-the-art techniques. |
| Databáze: | OpenAIRE |
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