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http://hdl.handle.net/11375/22275
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DC Field | Value | Language |
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dc.contributor.advisor | McNicholas, Paul D. | - |
dc.contributor.author | Roick, Tyler | - |
dc.date.accessioned | 2017-10-18T16:22:55Z | - |
dc.date.available | 2017-10-18T16:22:55Z | - |
dc.date.issued | 2017 | - |
dc.identifier.uri | http://hdl.handle.net/11375/22275 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.subject | Clustering | en_US |
dc.subject | Mixture Models | en_US |
dc.subject | Discrete Valued Time Series | en_US |
dc.title | Clustering Discrete Valued Time Series | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Mathematics and Statistics | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Master of Science (MSc) | en_US |
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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