A Novel algorithm for time series model selection: mutual information model selection algorithm (MIMSA

Date

2017

Author

Akça, Elif

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Time series model selection has gained a signiﬁcant popularity,and a variety of methods has been introduced in the recent years. It is crucial for a method to propose a candidate model as the ﬁnal model that explains the procedure underlying a series best and provides accurate forecasts among many candidates. In this study, it is aimed to create an algorithm for order selection in Box-Jenkins models that combines penalized natural logarithm of mutual information among the original series and predictions coming from each candidate. The penalization is achieved by subtracting the number of parameters in each candidate and empirical information the data provide. Simulation studies under various scenarios and application on a real data set imply that our algorithm offers a promising and satisfactory alternative to its counterparts.

Subject Keywords

Time-series analysis., Mathematical statistics., Box-Jenkins forecasting.

URI

http://etd.lib.metu.edu.tr/upload/12621150/index.pdf
https://hdl.handle.net/11511/26602

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

E. Akça, “A Novel algorithm for time series model selection: mutual information model selection algorithm (MIMSA,” M.S. - Master of Science, Middle East Technical University, 2017.