Forecasting air passenger demand with a new hybrid ensemble approach

Abstract Analyzing and modeling passenger demand dynamic, which has important implications on the management and the operation in the entire aviation industry, are deemed to be a tough challenge. Air passenger demand, however, exhibits consistently complex non-linearity and non-stationarity. To capture more precisely the aforementioned complex behavior, this paper proposes a hybrid approach VMD-ARMA/KELM-KELM for the short-term forecasting, which consists of variational mode decomposition (VMD), autoregressive moving average model (ARMA) and kernel extreme learning machine (KELM). First, VMD is adopted to decompose the original data into several mode functions so as to reduce their complexity. Then, the unit root test (ADF test) is employed to classify all the modes into the stable and unstable series. Meanwhile, the ARMA and the KELM models are used to forecast both the stationary and non-stationary components, respectively. Lastly, the final result is integrated by another KELM model incorporating the forecasting results of all components. In order to prove and verify the feasibility and robustness of the proposed approach, the passenger demands of Beijing, Guangzhou and Pudong airports are introduced to test the performance. Also, the experimental results show that the novel approach does have a more obviously advantage than other benchmark models regarding both accuracy and robustness analysis. Therefore, this approach can be utilized as a convincing tool for the air passenger demand forecasting.

[1]  Der-Horng Lee,et al.  Short-term freeway traffic flow prediction : Bayesian combined neural network approach , 2006 .

[2]  Shaolong Sun,et al.  A novel hybrid decomposition-ensemble model based on VMD and HGWO for container throughput forecasting , 2018 .

[3]  E. Vercher,et al.  Holt–Winters Forecasting: An Alternative Formulation Applied to UK Air Passenger Data , 2007 .

[4]  Michael J Demetsky,et al.  TRAFFIC FLOW FORECASTING: COMPARISON OF MODELING APPROACHES , 1997 .

[5]  Dianhui Wang,et al.  Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..

[6]  Jing Zhao,et al.  An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed , 2016 .

[7]  Bin Li,et al.  An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures , 2014, TheScientificWorldJournal.

[8]  Rui Wang,et al.  Research and Application of a Novel Hybrid Model Based on Data Selection and Artificial Intelligence Algorithm for Short Term Load Forecasting , 2017, Entropy.

[9]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[10]  Frank S. Koppelman,et al.  Predicting air travelers’ no-show and standby behavior using passenger and directional itinerary information , 2004 .

[11]  H. M. Zhang,et al.  RECURSIVE PREDICTION OF TRAFFIC CONDITIONS WITH NEURAL NETWORK MODELS , 2000 .

[12]  Shaolong Sun,et al.  Application of decomposition-ensemble learning paradigm with phase space reconstruction for day-ahead PM2.5 concentration forecasting. , 2017, Journal of environmental management.

[13]  Jing Zhao,et al.  A novel model: Dynamic choice artificial neural network (DCANN) for an electricity price forecasting system , 2016, Appl. Soft Comput..

[14]  Bart van Arem,et al.  Recent advances and applications in the field of short-term traffic forecasting. , 1997 .

[15]  Douglas C. Baker,et al.  Regional aviation and economic growth : cointegration and causality analysis in Australia , 2015 .

[16]  P C Vythoulkas,et al.  ALTERNATIVE APPROACHES TO SHORT TERM TRAFFIC FORECASTING FOR USE IN DRIVER INFORMATION SYSTEMS , 1993 .

[17]  Kwok-Leung Tsui,et al.  Nonlinear vector auto-regression neural network for forecasting air passenger flow , 2019, Journal of Air Transport Management.

[18]  Søren L. Buhl,et al.  How (In)accurate Are Demand Forecasts in Public Works Projects?: The Case of Transportation , 2005, 1303.6654.

[19]  Seraj Yousef Abed,et al.  An econometric analysis of international air travel demand in Saudi Arabia , 2001 .

[20]  Billy M. Williams,et al.  Urban Freeway Traffic Flow Prediction: Application of Seasonal Autoregressive Integrated Moving Average and Exponential Smoothing Models , 1998 .

[21]  Kin Keung Lai,et al.  Short-term forecasting of air passenger by using hybrid seasonal decomposition and least squares support vector regression approaches , 2014 .

[22]  Yufang Wang,et al.  A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting , 2016 .

[23]  Yanxue Wang,et al.  Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system , 2015 .

[24]  Calyampudi R. Rao,et al.  Generalized inverse of a matrix and its applications , 1972 .

[25]  Chaug-Ing Hsu,et al.  Application of Grey theory and multiobjective programming towards airline network design , 2000, Eur. J. Oper. Res..

[26]  Salim Lahmiri,et al.  A variational mode decompoisition approach for analysis and forecasting of economic and financial time series , 2016, Expert Syst. Appl..

[27]  Young-Ihn Lee,et al.  Short-term travel speed prediction models in car navigation systems , 2006 .

[28]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[29]  A. E. Hoerl,et al.  Ridge regression: biased estimation for nonorthogonal problems , 2000 .

[30]  Lei Chen,et al.  Enhanced random search based incremental extreme learning machine , 2008, Neurocomputing.

[31]  Siem Jan Koopman,et al.  A non-Gaussian generalization of the Airline model for robust seasonal adjustment , 2006 .

[32]  Franz Rothlauf,et al.  Gravity models for airline passenger volume estimation , 2007 .

[33]  Kin Keung Lai,et al.  A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting , 2014 .

[34]  Elton Fernandes,et al.  The causal relationship between GDP and domestic air passenger traffic in Brazil , 2010 .

[35]  Lee D. Han,et al.  AADT prediction using support vector regression with data-dependent parameters , 2009, Expert Syst. Appl..

[36]  Chu Zhang,et al.  A hybrid model based on synchronous optimisation for multi-step short-term wind speed forecasting , 2018 .

[37]  Rico Merkert,et al.  The causal relationship between air transport and economic growth: Empirical evidence from South Asia , 2016 .

[38]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[39]  M. Wolters,et al.  Comparative analysis of government forecasts for the Lisbon Airport , 2010 .

[40]  Huayou Chen,et al.  Carbon price forecasting with variational mode decomposition and optimal combined model , 2019, Physica A: Statistical Mechanics and its Applications.

[41]  Jianzhou Wang,et al.  Research and application of a novel hybrid forecasting system based on multi-objective optimization for wind speed forecasting , 2017 .

[42]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[43]  Yan Hao,et al.  The study and application of a novel hybrid system for air quality early-warning , 2019, Appl. Soft Comput..

[44]  A. Gilbey,et al.  Forecasting of Hong Kong airport's passenger throughput , 2014 .

[45]  Hugo M. Repolho,et al.  Air transportation demand forecast through Bagging Holt Winters methods , 2017 .

[46]  Guang-Bin Huang,et al.  Convex Incremental Extreme Learning Machine , 2007 .

[47]  Zhengyan Shao,et al.  A new semiparametric and EEMD based framework for mid-term electricity demand forecasting in China: Hidden characteristic extraction and probability density prediction , 2015 .

[48]  Anthony T.H. Chin,et al.  Developments in air transport: implications on investment decisions, profitability and survival of Asian airlines , 2001 .

[49]  Seongdo Kim,et al.  Forecasting short-term air passenger demand using big data from search engine queries , 2016 .