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http://dx.doi.org/10.5351/KJAS.2009.22.5.1007

A Study on Air Demand Forecasting Using Multivariate Time Series Models  

Hur, Nam-Kyun (Department of Business Administration, Korea Aerospace University)
Jung, Jae-Yoon (Department of Statistics, Chung-Ang University)
Kim, Sahm (Department of Statistics, Chung-Ang University)
Publication Information
The Korean Journal of Applied Statistics / v.22, no.5, 2009 , pp. 1007-1017 More about this Journal
Abstract
Forecasting for air demand such as passengers and freight has been one of the main interests for air industries. This research has mainly focus on the comparison the performance between the univariate seasonal ARIMA models and the multivariate time series models. In this paper, we used real data to predict demand on international passenger and freight. And multivariate time series models are better than the univariate models based on the accuracy criteria.
Keywords
Seasonal ARIMA model; VAR model; forecasting; air demand;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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