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http://hdl.handle.net/11375/22275
Title: | Clustering Discrete Valued Time Series |
Authors: | Roick, Tyler |
Advisor: | McNicholas, Paul D. |
Department: | Mathematics and Statistics |
Keywords: | Clustering;Mixture Models;Discrete Valued Time Series |
Publication Date: | 2017 |
Abstract: | There is a need for the development of models that are able to account for discreteness in data, along with its time series properties and correlation. A review of the application of thinning operators to adapt the ARMA recursion to the integer-valued case is first discussed. A class of integer-valued ARMA (INARMA) models arises from this application. Our focus falls on INteger-valued AutoRegressive (INAR) type models. The INAR type models can be used in conjunction with existing model-based clustering techniques to cluster discrete valued time series data. This approach is then illustrated with the addition of autocorrelations. With the use of a finite mixture model, several existing techniques such as the selection of the number of clusters, estimation using expectation-maximization and model selection are applicable. The proposed model is then demonstrated on real data to illustrate its clustering applications. |
URI: | http://hdl.handle.net/11375/22275 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Roick_Tyler_2017August_MSc.pdf | 1 MB | Adobe PDF | View/Open |
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