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Research on improvement and optimisation of modelling method of China’s civil aircraft market demand forecast model

Published online by Cambridge University Press:  14 April 2021

Y. Zhang*
Affiliation:
School of Aviation Northwestern Polytechnical UniversityXianChina
K. Cao*
Affiliation:
School of Aviation Northwestern Polytechnical UniversityXianChina
W. Dong*
Affiliation:
China COMAC Shanghai Aircraft Design and ResearchShanghaiChina

Abstract

With the development of China’s economy, China’s aviation market has expanded, and related industries have also developed rapidly. For the long-term development of the industry, many countries and enterprises began to make demand forecasts with different levels for the product market. The same is true for China’s civil aircraft-related industries. There are a variety of predictive models, but not all of them are appropriate for the prediction of civil aircraft market demand. This paper introduces a variety of modelling methods for forecasting models, including time series forecasting models and causal analysis forecasting models. The contribution of our work is the adoption of a new coefficient determination method to establish a variable-weight combination forecasting model, which greatly improves the forecasting accuracy. In addition, we also propose a new and more stable prediction model, the chain prediction model. Simulation prediction is carried out for each model in this work. Through the analysis and comparison of the prediction results, we conclude that the prediction effects of the variable weight combination prediction model and the chain prediction model are superior to those of other single prediction models. The chain prediction model in particular has better performance in medium- and long-term prediction, compared with the other prediction models. Finally, the model is applied to predict the demand of Chinese civil aircraft in the next 20 years, which confirms that the Chinese civil aircraft market will expand in the future.

Type
Research Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

Cao, Y. Economic development, market transition, and work values in post-socialist China, Soc Forces, 2020, published online 5 March. doi: 10.1093/sf/soaa001CrossRefGoogle Scholar
Lin, J. Network analysis of China’s aviation system, statistical and spatial structure, J Transp Geogr, 2012, 22, (12), pp 109117. doi: 10.1016/j.jtrangeo.2011.12.002CrossRefGoogle Scholar
Liu, X., Hang, Y., Wang, Q. and Zhou, D. Flying into the future: a scenario-based analysis of carbon emissions from China’s civil aviation, J Air Transp Manag, 2020, 85, (8), p 101793. doi: 10.1016/j.jairtraman.2020.101793CrossRefGoogle Scholar
Ellison, A.P. and Stafford, E.M. The future world demand for civil aircraft, Aeronaut J, 1974, 78, (767), pp 506512. doi: 10.1017/S0001924000037544Google Scholar
Ito, S. Forecast methodology of the demand for civil air transports and determination of the market requirements, J Jpn Soc Aeronaut Space Sci, 1978, 26, (294), pp 356358. doi: 10.2322/jjsass1969.26.356Google Scholar
Brown, D.G. Requirements for future civil aircraft, Aeronaut J, 1969, 73, (701), pp 397407. doi: 10.1017/S0001924000052714CrossRefGoogle Scholar
Franz, K., Hrnschemeyer, R., Ewert, A., Fromhold-Eisebith, M.and Reichmuth, J. Life cycle engineering in preliminary aircraft design, Proceedings of the 19th CIRP Conference on Life Cycle Engineering, 2012, University of California at Berkeley, pp 473478 doi:10.1007/978-3-642-29069-5_80CrossRefGoogle Scholar
Anker, R. Comparison of airbus, Boeing, Rolls-Royce and AVITAS market forecasts, Air & Space Europe, 2000, 2, (3), pp 49. doi: 10.1016/S1290-0958(00)80052-0CrossRefGoogle Scholar
Bhadra, D. Demand for air travel in the United States: bottom-up econometric estimation and implications for forecasts by origin-destination pairs, J Air Transport, 2003, 8, (2), pp 19. doi: 10.2514/6.2002-5861Google Scholar
Profillidis, V.A. Econometric and fuzzy models for the forecast of demand in the airport of Rhodes, J Air Transport Manag, 2000, 6, (2), pp 95100. doi: 10.1016/S0969-6997(99)00026-5CrossRefGoogle Scholar
Rengaraju, V.R. and Arasan, V.T. Model for air travel demand, J Transport Eng, 1992, 118, (3), pp 371380. doi: 10.1061/(ASCE)0733-947X(1992)118:3(371)CrossRefGoogle Scholar
Coldren, G.M., Koppelman, F.S., Kasturirangan, K. and Mukherjee, A. Modeling aggregate air-travel itinerary shares: logit model development at a major US airline, J Air Transport Manag, 2003, 9, (6), pp 361369. doi: 10.1016/S0969-6997(03)00042-5CrossRefGoogle Scholar
Sun, H. and Shi, H. Research on forecast model of airline transportation demand, J CAFUC, 2004, 15, (5), pp 3840. doi: 10.3969/j.issn.1009-4288.2004.05.012Google Scholar
Wang, X., Fan, W., Wu, T. and Chi, H. Flight demand forecasting model based on BP neural network, J CAFUC, 2004, 22, (6), pp 4449. doi: 10.3969/j.issn.1001-5590.2004.06.011Google Scholar
Zhang, Y. and Zhang, L. Comparative analysis of the share model and grey model in route demand forecasting, Stat Decis Making, 2007, (18), pp 142144. doi: 10.3969/j.issn.1002-6487.2007.18.053Google Scholar
Zhang, C., Guo, Y., Market research and analysis forecast, Tsinghua University Press, 2013, Beijing, pp 323345.Google Scholar
Torralbo, M. and Time series of scientific growth in Spanish doctoral theses (1848–2009), Scientometrics, 2012, 91, (1), pp 1536. doi: 10.1007/s11192-011-0572-xGoogle Scholar
Javed, S.A. and Liu, S. Predicting the research output/growth of selected countries: application of Even GM (1, 1) and NDGM models, Scientometrics, 2020, published online 25 November. doi: 10.1007/s11192-017-2586-5Google Scholar
Cai, S. Empirical analysis of GM(1,N) application of first order multivariable grey prediction model, J Harbin Normal University Natural Sci, 2019, 35, (1), pp 3135. doi: 10.3969/j.issn.1000-5617.2019.01.006Google Scholar
Young, W.L. The Box-Jenkins approach to time series analysis and forecasting: principles and applications, RAIRO - Oper Res, 1977, 11, (2), pp 129143. doi: 10.1051/ro/1977110201291CrossRefGoogle Scholar
Narendra, B.C. and Eswara, R.B. Prediction of selected indian stock using a partitioning–interpolation based ARIMA–GARCH model, Appl Comput Informa, 2015, 11, (2), pp 130143. doi: 10.1016/j.aci.2014.09.002CrossRefGoogle Scholar
Maia, C.and Goncalves, M. Application of switched adaptive system to load forecasting, Electr Pow Syst Res, 2008, 78, (4), pp 721727. doi: 10.1016/j.epsr.2007.05.014CrossRefGoogle Scholar
Gardner, E.S. Exponential smoothing: the state of the art—part II, Int J Forecast, 2006, 22, (4), pp 637666. doi: 10.1016/j.ijforecast.2006.03.005CrossRefGoogle Scholar
Shone, M.L. Exponential smoothing with an adaptive response rate, J Oper Res Soc, 1967, 18, (1), pp 5359. doi: 10.2307/3010768CrossRefGoogle Scholar
Newcome, L.R. Unmanned aviation traffic forecast, Aeronaut J, 2009, 113, (1145), pp 459466. doi: 10.1017/S0001924000003122CrossRefGoogle Scholar
Catalina, T., Iordache, V. and Caracaleanu, B. Multiple regression model for fast prediction of the heating energy demand. Energy Build, 2013, 57, pp 302312. doi: 10.1016/j.enbuild.2012.11.010CrossRefGoogle Scholar
Yoshikane, F. Multiple regression analysis of a patent’s citation frequency and quantitative characteristics: the case of Japanese patents, Scientometrics, 2013, 96, (1), pp 365379. doi: 10.1007/s11192-013-0953-4CrossRefGoogle Scholar
Çetek, F.A., Kantar, Y.M. and Cavcar, A. A regression model for terminal airspace delays, Aeronaut J, 2017, 121, (1239), pp 680692. doi: 10.1017/aer.2017.19Google Scholar
Wu, L. and Liu, S. Using weighted partial least squares to estimate the development cost of complex equipment at early design stage, IEEE International Conference on Grey Systems & Intelligent Services, 2015. doi: 10.1109/GSIS.2015.7301922Google Scholar
Curran, R., Raghunathan, S. and Price, M. Review of aerospace engineering cost modelling: the genetic causal approach, Prog Aerosp Sci, 2004, 40, (8), pp 487534. doi: 10.1016/j.paerosci.2004.10.001CrossRefGoogle Scholar
Lakshminarayanan, S. An integrated stock market forecasting model using neural networks. IJFBMI, 2011, 1, (1), pp 3049. doi: 10.1504/IJBFMI.2008.020813Google Scholar
Bates, JM. and Granger, C.W.J. The combination of forecasts. J Oper Res Soc, 1969, 20, (4), pp 451468. doi: 10.2307/3008764CrossRefGoogle Scholar
Diebold, F.X. and Pauly, P. Structural change and the combination of forecasts. J Forecast, 2010, 6, (1), pp 2140. doi: 10.2307/3008764CrossRefGoogle Scholar
Zhang, G.P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 2003, 50, (17), pp 159175. doi: 10.1016/S0925-2312(01)00702-0CrossRefGoogle Scholar
Chen, X., Zhu, G. and Qu, D. Application of combined model based on variable weight for prediction of highway freight volume, Third International Conference on Digital Manufacturing & Automation (ICDMA), 2012. IEEE Computer Soc. doi: 10.1109/ICDMA.2012.59Google Scholar
Chen, H. Validity theory and application of combination forecasting method. Science Press, 2008, Beijing.Google Scholar
Wang, J. Research on demand forecasting method of civil aircraft market, Civil Aircraft Design and Research, 2013, (3), pp 6770. doi: 10.3969/j.issn.1674-9804.2013.03.017Google Scholar
Statistics of the number of civil aircraft (frames) [EB/OL], 2019. URL; https://data.stats.gov. cn/easyquery.htm?cn=C01&zb=A0G0R&sj=2019Google Scholar
Yang, Z.L., Li, Y., Song, Y.W. and Chen, X. Empirical study of robust combination of forecasts for short-term highway traffic flow forecast, International Conference on Machine Learning & Cybernetics, 2012, IEEE. doi: 10.1109/ICMLC.2012.6359565Google Scholar
Wu, B., Zhang, W., Guo, Z. and Wang, Z. Research on forecasting model of gas emission in coal mining and heading face based on ARIMA-GM method, The 3rd International Conference on Machinery, Materials Sci and Energy Engineering (ICMMSEE 2015), 2015. doi: 10.1142/9789814719391_0081CrossRefGoogle Scholar
Zhang, P. Comparison of variable weight and weight determination in combination forecasting, Stat Decis Making, 2018, 34, (17), pp 8082. doi: 10.13546/j.cnki.tjyjc.2018.17.019Google Scholar
Boeing Company. Current market outlook 2018–2037[EB/OL], 2018. URL; http://www.boeing.com/commercial/market/commercial-market-outlook/Google Scholar
China Commercial Aircraft Corporation. 2018–2037 market forecast annual report [EB/OL], 2018. URL; http://www.comac.cc/xwzx/gsxw/201811/06/t20181106_6600647.shtmlGoogle Scholar
China Aviation Industry Corporation. 2018$\sim$2037 civil aircraft china market forecast annual report [EB/OL], 2018. URL; http://www.yidianzixun.com/article/0KSmvbZJGoogle Scholar