Coresets for Time Series Clustering

Lingxiao Huang, K. Sudhir () and Nisheeth Vishnoi ()
Additional contact information
Lingxiao Huang: Huawei TCS Lab
K. Sudhir: Cowles Foundation and Yale School of Management, https://som.yale.edu/faculty/k-sudhir
Nisheeth Vishnoi: Cowles Foundation and Yale Department of Computer Science, https://www.cs.yale.edu/homes/vishnoi/Home.html

No 2310, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: We study the problem of constructing coresets for clustering problems with time series data. This problem has gained importance across many fields including biology, medicine, and economics due to the proliferation of sensors for real-time measurement and rapid drop in storage costs. In particular, we consider the setting where the time series data on N entities is generated from a Gaussian mixture model with autocorrelations over k clusters in Rd. Our main contribution is an algorithm to construct coresets for the maximum likelihood objective for this mixture model. Our algorithm is efficient, and, under a mild assumption on the covariance matrices of the Gaussians, the size of the coreset is independent of the number of entities N and the number of observations for each entity, and depends only polynomially on k, d and 1/ε, where ε is the error parameter. We empirically assess the performance of our coresets with synthetic data.

Pages: 26 pages
Date: 2021-11
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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