Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach

Research output: Journal PublicationArticlepeer-review

62 Citations (Scopus)

Abstract

In this study, a novel SARIMA-SVR model is proposed to forecast statistical indicators in the aviation industry that can be used for later capacity management and planning purpose. First, the time series is analysed by SARIMA. Then, Gaussian White Noise is reversely calculated. Next, four hybrid models are proposed and applied to forecast the future statistical indicators in the aviation industry. The results of empirical study suggest that one of the proposed models, namely SARIMA_SVR3, can achieve better accuracy than other methods, and prove that incorporating Gaussian White Noise is able to increase forecasting accuracy.

Original languageEnglish
Pages (from-to)169-180
Number of pages12
JournalTransportation Research Part E: Logistics and Transportation Review
Volume122
DOIs
Publication statusPublished - Feb 2019

Keywords

  • Aviation industry
  • Gaussian white noise
  • SARIMA
  • SVR
  • Time series forecasting

ASJC Scopus subject areas

  • Business and International Management
  • Civil and Structural Engineering
  • Transportation

Fingerprint

Dive into the research topics of 'Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach'. Together they form a unique fingerprint.

Cite this