Yalaz, S.Taylan, P.Ozkurt, F. YerlikayaIndustrial Engineering2024-07-052024-07-0520190972-05102169-001410.1080/09720510.2019.1606320https://doi.org/10.1080/09720510.2019.1606320https://hdl.handle.net/20.500.14411/2791In 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.eninfo:eu-repo/semantics/closedAccessTime seriesMultivariate adaptive regression splines (MARS)Adaptive splines threshold autoregression (ASTAR)Tikhonov regularizationMultiobjective optimizationConic quadratic programming (CQP)Article22611271142WOS:0007473447000012