A new approach to adaptive spline threshold autoregression by using Tikhonov regularization and continuous optimization

In this study adaptive spline threshold autoregression and conic quadratic programming is used to develope conic adaptive spline threshold autoregression. With the introduced approach the second stepwise algorithm of adaptive spline threshold autoregression model turned to the Tikhonov regularizatio...

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Bibliographic Details
Published inJournal of statistics & management systems Vol. 22; no. 6; pp. 1127 - 1142
Main Authors Yalaz, S., Taylan, P., Özkurt, F. Yerlikaya
Format Journal Article
LanguageEnglish
Published New Delhi Taylor & Francis 18.08.2019
Taru Publications
Subjects
Adaptive algorithms
Adaptive splines threshold autoregression (ASTAR)
Conic quadratic programming (CQP)
Inflation
Multiobjective optimization
Multiple objective analysis
Multivariate adaptive regression splines (MARS)
Optimization
Quadratic programming
Regression analysis
Regularization
Tikhonov regularization
Time series
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Summary:In this study adaptive spline threshold autoregression and conic quadratic programming is used to develope conic adaptive spline threshold autoregression. With the introduced approach the second stepwise algorithm of adaptive spline threshold autoregression model turned to the Tikhonov regularization problem which was transformed into conic quadratic programming problem. The aim is to attain an optimum solution chosen in many solutions obtained by determining the bounds of the optimization problem using multiobjective optimization approach. Furthermore, in application part we used two different data set to compare performances of linear regression, adaptive spline threshold autoregression and conic adaptive spline threshold autoregression approaches.
ISSN:0972-0510
2169-0014
DOI:10.1080/09720510.2019.1606320