Mutual information model selection algorithm for time series

Date

2020-09-01

Author

Akca, Elif
Yozgatlıgil, Ceylan

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Time series model selection has been widely studied in recent years. It is of importance to select the best model among candidate models proposed for a series in terms of explaining the procedure that governs the series and providing the most accurate forecast for the future observations. 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 applications on real data sets imply that our algorithm offers a promising and satisfactory alternative to its counterparts.

Subject Keywords

Statistics, Probability and Uncertainty, Statistics and Probability

URI

https://hdl.handle.net/11511/38421

Journal

JOURNAL OF APPLIED STATISTICS

DOI

https://doi.org/10.1080/02664763.2019.1707516

Collections

Department of Statistics, Article

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Citation Formats

E. Akca and C. Yozgatlıgil, “Mutual information model selection algorithm for time series,” JOURNAL OF APPLIED STATISTICS, pp. 2192–2207, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/38421.