Determining optimal architecture for dynamic linear models in time series applications
- UNCW Author/Contributor (non-UNCW co-authors, if there are any, appear on document)
- Kathleen Mary Karlon (Creator)
- Institution
- The University of North Carolina Wilmington (UNCW )
- Web Site: http://library.uncw.edu/
- Advisor
- Edward Boone
Abstract: This work is focused on assessing the performance of one particular time series
forecasting paradigm: Dynamic Linear Models (DLM). This research extends the
M3 forecasting competition, a large-scale project to assess the e±cacy of various
forecasting methods and also that of the research done in [14]. This work provides
insight into the performance of the DLM against the model architecture. Symmetric
Mean Absolute Percentage Error and Linear Mixed Models are used to analyze the
competition results, which showed that paradigm performance is dependent upon
the class of time series. Furthermore, in some cases, the chosen DLM models from
this work outperform optimal models from [14]. This work explores di®erent DLM
models and compares the results with previously chosen models to determine if the
models from this work outperform other models.
Determining optimal architecture for dynamic linear models in time series applications
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Created on 1/1/2009
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Additional Information
- Publication
- Thesis
- A Thesis Submitted to the University of North Carolina at Wilmington in Partial Fulfillment of the Requirement for the Degree of Masters of Science
- Language: English
- Date: 2009
- Keywords
- Linear models (Statistics), Time-series analysis
- Subjects
- Time-series analysis
- Linear models (Statistics)