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

No Thumbnail Available

Date

2019

Authors

Journal Title

Journal ISSN

Volume Title

Publisher

Taru Publications

Open Access Color

OpenAIRE Downloads

OpenAIRE Views

Research Projects

Organizational Units

Thumbnail Image
Organizational Unit
Industrial Engineering
(1998)
Industrial Engineering is a field of engineering that develops and applies methods and techniques to design, implement, develop and improve systems comprising of humans, materials, machines, energy and funding. Our department was founded in 1998, and since then, has graduated hundreds of individuals who may compete nationally and internationally into professional life. Accredited by MÜDEK in 2014, our student-centered education continues. In addition to acquiring the knowledge necessary for every Industrial engineer, our students are able to gain professional experience in their desired fields of expertise with a wide array of elective courses, such as E-commerce and ERP, Reliability, Tabulation, or Industrial Engineering Applications in the Energy Sector. With dissertation projects fictionalized on solving real problems at real companies, our students gain experience in the sector, and a wide network of contacts. Our education is supported with ERASMUS programs. With the scientific studies of our competent academic staff published in internationally-renowned magazines, our department ranks with the bests among other universities. IESC, one of the most active student networks at our university, continues to organize extensive, and productive events every year.

Journal Issue

Abstract

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.

Description

Keywords

Time series, Multivariate adaptive regression splines (MARS), Adaptive splines threshold autoregression (ASTAR), Tikhonov regularization, Multiobjective optimization, Conic quadratic programming (CQP)

Turkish CoHE Thesis Center URL

Fields of Science

Citation

WoS Q

Scopus Q

Source

Volume

22

Issue

6

Start Page

1127

End Page

1142

URI

https://doi.org/10.1080/09720510.2019.1606320
 https://hdl.handle.net/20.500.14411/2791

Collections

WOS