Abstract
Along with the increases of 3G relevant products and the updating regulations of 3G phones, 3G phones are gradually replacing 2G phones as the mainstream product in Taiwan. Therefore, accurate 3G phones demand forecasting is necessary for those communication related enterprises. Recently,support vector regression (SVR) has been successfully applied to solve nonlinear regression and time series problems. This investigation presents a 3G phones demand forecasting model which combines chaotic sequence with genetic algorithm to improve the forecasting performance. Subsequently, a numerical example of 3G phones demand data from Taiwan is used to illustrate the proposed SVRCGA model. The empirical results reveal that the proposed model outperforms the other three existed models, namely the autoregressive integrated moving average (ARIMA) model, the general regression neural networks (GRNN) model, and SVRGASA model.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Sheldon, P.J., Var, T.: Tourism forecasting: A review of empirical research. Journal of Forecasting 4, 183–195 (1985)
Box, G.E.P., Jenkins, G.M.: Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco (1976)
Witt, S.F., Witt, C.A.: Modeling and Forecasting Demand in Tourism. Academic Press, London (1992)
Suykens, J.A.K.: Nonlinear modelling and support vector machines. In: Proceedings of IEEE Instrumentation and Measurement Technology Conference, pp. 287–294 (2001)
Vapnik, V., Golowich, S., Smola, A.: Support vector machine for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems 9, 281–287 (1996)
Hung, W.M., Hong, W.C.: Application of SVR with improved ant colony optimization algorithms in exchange rate forecasting. Control and Cybernetics 38, 863–891 (2009)
Pai, P.F., Lin, C.S., Hong, W.C., Chen, C.T.: A hybrid support vector machine regression for exchange rate prediction. International Journal of Information and Management Sciences 17, 19–32 (2006)
Pai, P.F., Hong, W.C., Chang, P.T., Chen, C.T.: The application of support vector machines to forecast tourist arrivals in Barbados: An empirical study. International Journal of Management 23, 375–385 (2006)
Hong, W.C.: Rainfall forecasting by technological machine learning models. Applied Mathematics and Computation 200, 41–57 (2008)
Pai, P.F., Hong, W.C.: A recurrent support vector regression model in rainfall forecasting. Hydrological Processes 21, 819–827 (2007)
Hong, W.C.: Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm. Neurocomputing 74, 2096–2107 (2011)
Hong, W.C., Dong, Y., Zheng, F., Wei, S.Y.: Hybrid evolutionary algorithms in a SVR traffic flow forecasting model. Applied Mathematics and Computation 217, 6733–6747 (2011)
Hong, W.C.: Hybrid evolutionary algorithms in a SVR-based electric load forecasting model. International Journal of Electrical Power and Energy Systems 31, 409–417 (2009)
Hong, W.C.: Application of chaotic ant swarm optimization in electric load forecasting. Energy Policy 38, 5830–5839 (2010)
Hong, W.C.: Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model. Energy Conversion and Management 50, 105–117 (2009)
Cherkassky, V., Ma, Y.: Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 17, 113–126 (2004)
Wang, L., Zheng, D.Z., Lin, Q.S.: Survey on chaotic optimization methods. Computing Technology and Automation 20, 1–5 (2000)
Li, B., Jiang, W.: Optimizing complex functions by chaos search. Cybernetics and Systems 29, 409–419 (1998)
Lorenz, E.N.: Deterministic nonperiodic flow. Journal of The Atmospheric Sciences 20, 130–141 (1963)
Yuan, X., Yuan, Y., Zhang, Y.: A hybrid chaotic genetic algorithm for short-term hydro system scheduling. Mathematics and Computers in Simulation 59, 319–327 (2002)
Cheng, C.T., Wang, W.C., Xu, D.M., Chau, K.W.: Optimizing hydropower reservoir operation using hybrid genetic algorithm and chaos. Water Resources Management 22, 895–909 (2008)
Chunghwa Telecom Co. Ltd. Monthly Revenue Reports. Financial Information Service (2008), http://www.cht.com.tw/CompanyCat.php?Page=FileDownload&CatID=798
Hong, W.C., Dong, Y., Chen, L.Y., Lai, C.Y.: Taiwanese 3G mobile phone demand forecasting by SVR with hybrid evolutionary algorithms. Expert Systems with Applications 37, 4452–4462 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, LY., Hong, WC., Panigrahi, B.K., Wei, S.Y. (2011). SVR with Chaotic Genetic Algorithm in Taiwanese 3G Phone Demand Forecasting. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27172-4_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-27172-4_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27171-7
Online ISBN: 978-3-642-27172-4
eBook Packages: Computer ScienceComputer Science (R0)