A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting

Yi Xiao, John J. Liu, Yi Hu, Yingfeng Wang, Kin Keung Lai and Shouyang Wang

Journal of Air Transport Management, 2014, vol. 39, issue C, 1-11

Abstract: Air transport demand forecasting is receiving increasing attention, especially because of intrinsic difficulties and practical applications. Total passengers are used as a proxy for air transport demand. However, the air passenger time series usually has a complex behavior due to their irregularity, high volatility and seasonality. This paper proposes a new hybrid approach, combining singular spectrum analysis (SSA), adaptive-network-based fuzzy inference system (ANFIS) and improved particle swarm optimization (IPSO), for short-term air passenger traffic prediction. The SSA is used for identifying and extracting the trend and seasonality of air transport demand and the artificial intelligence technologies, including ANFIS and IPSO, are utilized to deal with the irregularity and volatility of the demand. The HK air passenger data are collected to establish and validate the forecasting model. Empirical results clearly points to the enormous potential that the proposed approach possesses in air transport demand forecasting and can be considered as a viable alternative.

Keywords: Air transport demand forecasting; Singular spectrum analysis; Adaptive-network-based fuzzy inference system; Particle swarm optimization (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jaitra:v:39:y:2014:i:c:p:1-11

DOI: 10.1016/j.jairtraman.2014.03.004

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