Clustering High-Dimensional Time Series Based on Parallelism

• Autores: Zhang Ting
• Localización: Journal of the American Statistical Association, ISSN 0162-1459, Vol. 108, Nº 502, 2013, págs. 577-588
• Idioma: inglés
• DOI: 10.1080/01621459.2012.760458
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• Resumen

  • This article considers the problem of clustering high-dimensional time series based on trend parallelism. The underlying process is modeled as a nonparametric trend function contaminated by locally stationary errors, a special class of nonstationary processes. For each group where the parallelism holds, I semiparametrically estimate its representative trend function and vertical shifts of group members, and establish their central limit theorems. An information criterion, consisting of in-group similarities and number of groups, is then proposed for the purpose of clustering. I prove its theoretical consistency and propose a splitting-coalescence algorithm to reduce the computational burden in practice. The method is illustrated by both simulation and a real-data example.