Because many time series usually contain both linear and nonlinear components, a single linear or nonlinear model may be insufficient for modeling and predicting time series. Therefore, estimation results are tried to be improved by using collaborative models in time series shortterm prediction processes. In this study, the performances of both standalone models and models whose different combinations can be used in a hybrid environment are compared. The mean absolute percentage error (MAPE) metric values obtained from two different categories were evaluated. In addition, the estimation performances of three different approaches such as equiweighted (EW), variableweighted (VW) and crossvalidationweighted (CVW) for hybrid operation were also compared. The findings on the container throughput forecast of the Airpassengers dataset reveal that the hybrid model's forecasts outperform the noncombined model.
Time series analysis, shortterm prediction, hybrid models, nonhybrid models
Because many time series usually contain both linear and nonlinear components, a single linear or nonlinear model may be insufficient for modeling and predicting time series. Therefore, estimation results are tried to be improved by using collaborative models in time series shortterm prediction processes. In this study, the performances of both standalone models and models whose different combinations can be used in a hybrid environment are compared. The mean absolute percentage error (MAPE) metric values obtained from two different categories were evaluated. In addition, the estimation performances of three different approaches such as equiweighted (EW), variableweighted (VW) and crossvalidationweighted (CVW) for hybrid operation were also compared. The findings on the container throughput forecast of the Airpassengers dataset reveal that the hybrid model's forecasts outperform the noncombined model.
Time series analysis, shortterm prediction, hybrid models, nonhybrid models
Birincil Dil  İngilizce 

Konular  Mühendislik, Ortak Disiplinler 
Bölüm  Makaleler 
Yazarlar 

Destekleyen Kurum   
Proje Numarası   
Teşekkür   
Erken Görünüm Tarihi  28 Haziran 2022 
Yayımlanma Tarihi  28 Haziran 2022 
Yayınlandığı Sayı  Yıl 2022, Cilt 13, Sayı 2 