Skip to main content
SpringerLink
Log in

An Intelligent Hybrid System for Business Forecasting

  • Chapter
Intelligent Knowledge-Based Systems

  • 2387 Accesses

Abstract

Forecasting is the prediction of what is expected to happen in the future, especially in relation to a particular event or situation [1]. It is an integral part of decision-making activities of management. An organisation establishes goals and objectives, seeks to predict both internal and external environmental factors, then decides on actions that it hopes will result in attainment of these goals and objectives. The need for forecasting is increasing as management attempts to decrease its dependence on chance and becomes more scientific in dealing with its environments [2][3].

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 429.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 549.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Collins, “COLLINS COBUILD English Language Dictionary”, the Collins Birmingham University International Language Database, London and Glasgow, 1990.

    Google Scholar 

  2. Makridakes, S., Wheelwright, S.C. and Hyndman, R. J., “Forecasting Methods and Applications, Third Edition”, John Wiley & Sons Inc., New York, 1998.

    Google Scholar 

  3. Hanke, J. and Reitsch, A., “Business Forecasting, 4th ed.”, Boston, MA: Allyn and Bacon, 1992.

    Google Scholar 

  4. Colding, B., Colwell, L. V and Smith D. N., “Delphi Forecasts of Manufacturing Technology”, Michigan: Society of Manufacturing Engineers, 1978.

    Google Scholar 

  5. Huss, W R. and Honton, E. J., “Alternative Methods for Developing Business Scenarios”, Economics and Policy Analysis Occasional Paper, No. 55, Battelle, July 1986.

    Google Scholar 

  6. Gordon, T. J., Becker, H. S. and Gerjuoy, H., “Trend-Impact Analysis: A New Forecasting Tool”, The Futures Group, Glastonbury, CT, 1974.

    Google Scholar 

  7. Gordon, T. J., et al, “Initial Experiments with the Cross Impact Matrix Method of Forecasting”, Futures, Volume No.2, pp 100–116, December 1968.

    Article  Google Scholar 

  8. Honton, E. J., Stacey, G. S., and Millett S. M., “Future Scenarios: The BASICS Computational Method”, Economics and Policy Analysis Occasional Paper, No. 44, Battelle, July 1985.

    Google Scholar 

  9. Chandler, J. and Cockle, P, “Techniques of Scenario Planning”, McGRAW-HILL Book Company (UK) Limited, London, 1982.

    Google Scholar 

  10. Azoff, E. M., “Neural Network: Time Series Forecasting of Financial Markets”, John Wiley & Sons, Chichester, 1994.

    Google Scholar 

  11. Refenes, A. P, “Neural Networks in the Capital Markets”, John Wiley & Sons, Chichester, 1995.

    Google Scholar 

  12. Goonatilake, S. and Treleaven, P, “Intelligent Systems for Finance and Business”, John Wiley & Sons, 1995.

    Google Scholar 

  13. Goonatilake, S. and Khebbal, S., “Intelligent Hybrid Systems”, John Wiley & Sons, Chichester, 1995.

    Google Scholar 

  14. Otsuka, Y, Hanaoka, K., and Konishi, M., “Applications of Neural Network Models to Iron and Steel Making Process”, Proceedings of the 2 nd International Conference on Fuzzy Logic & Neural Networks, Iizuka, Japan, 1992.

    Google Scholar 

  15. Becraft, W. R., Lee, P. L. and Newell, R. B., “Integration of Neural Networks and Expert Systems for Process Fault Diagnosis”, 12 th International Conference on A1, Australia, Vol. 2, 1991.

    Google Scholar 

  16. Kirn, S., Scherer, A. and Schlageter, G., “Problem Solving in Federative Environments: The FRESCO Concept of Cooperative Agents”, in The New Generation of Information Systems: From Data to Knowledge; edited by M. P. Papazoglou; J. Zeleznikow, Springer-Verlag, Lecture Notes in A1, 1992.

    Google Scholar 

  17. Economist Publications, “A Neural Network is Watching You, the Year in’ 95”, 1994.

    Google Scholar 

  18. Holder, V. “Tackling insider dealing with fuzzy logic”, Financial Times, vol. 29, September, 1994.

    Google Scholar 

  19. Scherer, A. & Schlageter, G., “A multi-agent approach for the integration of neural networks and expert systems”, in Goonatilake, S. and Khebbal, S. (eds), Intelligent Hybrid Systems, John Wiley, Chichester, 1995.

    Google Scholar 

  20. Kang, S. Y., “An investigation of the use of feedforward neural networks for forecasting”, Ph.D Dissertation, KENT State Univ., UMI 1991.

    Google Scholar 

  21. Taylor, J. G. and Mannion, C., “Theory & Applications of Neural Networks,” Springer-Verlag, London Limited, 1992.

    MATH  Google Scholar 

  22. Zurada, J. M., “Introduction to Artificial Neural System”, West Publishing Company, USA, 1992.

    Google Scholar 

  23. Zadeh, L. A. “Fuzzy Sets”, Information and Control, Vol. 8, 1965.

    Google Scholar 

  24. Lin, C. T. and Lee, C. S. Georhe, “Neural-network-based fuzzy logic control and decision system”, IEEE Transactions on computers, Vol. 40, No 12, December, 1992.

    Google Scholar 

  25. Klimasauskas, C., “Using fuzzy pre-processing with neural networks for chemical process diagnostic problems”, in Goonatilake, S. and Khebbal, S. (eds), Intelligent Hybrid Systems, John Wiley, Chichester, 1995.

    Google Scholar 

  26. Adeli, H. and Hung, S. L., “Machine Learning: Neural Networks, Genetic Algorithm and Fuzzy Systems”, KJohn Wiley & Sons, Inc. New York, 1995.

    MATH  Google Scholar 

  27. Kosko, B., “Neural networks and fuzzy system”, Prentice-Hall International Editions, 1992a.

    Google Scholar 

  28. Kohonen, T., “Self-organization and Associative Memory”, Springer-Verlag, Berlin, 1988.

    MATH  Google Scholar 

  29. Rumelhart, D. E., Hinton, G. E. and Williams, R. J., “Learning internal representations by error propagation”, Parallel Distributed Processing, Vol. 1, MIT Press, Cambridge, 1986.

    Google Scholar 

  30. Bateman, R. E., Bowden, R. G., Gogg, T. J., Harrell, C. R. and Mott, J. R. A., “System Improvement Using Simulation”, Fifth Edition, Promodel Corporation, 1997.

    Google Scholar 

  31. Law, A. M. and Kelton, W. D., “Simulation Modeling & Analysis”, Second Edition, McGRAW-HILL International Editions, 1991.

    Google Scholar 

  32. AutoSimulations, Inc. “AutoMod Version 8.0”, January 1998.

    Google Scholar 

  33. Damodar, N. Gujarati, “Basic Econometrics”, Third Edition, McGRAW-HILL INTERNATIONAL EDITIONS, 1995.

    Google Scholar 

  34. SAS Institute Inc., “Base SAS Software, Version 6.0,” SAS Circle Box 8000, Cary, NC, USA, 1990.

    Google Scholar 

  35. Cheung, Y. W. and Chinn, M. D., “Integration, Cointegration and the Forecast Consistency of Structural Exchange Rate Models”, Working Paper Series 5943, National Bureau of Economic Research, 1997.

    Google Scholar 

  36. YIP, P. S. L., “Exchange Rate Volatility and Export Performance in Singapore”, Working Paper Series No: 10-94, Nanyang Technological University, Singapore, 1994.

    Google Scholar 

  37. Department of Statistics, “Yearbook of Statistics”, Singapore, 1990–1995.

    Google Scholar 

  38. Armstrong, J. S. and Collopy, F., “Error measures for generalizing about forecasting methods: Empirical comparisons”, International Journal of Forecasting, Vol. 8, 1992.

    Google Scholar 

  39. Fildes, Robert, “The evaluation of extrapolative forecasting methods”, Internationaljournal of Forecasting, Vol. 8, 1992.

    Google Scholar 

  40. Makridakis, S. et al., “The accuracy of extrapolation (time series) methods: Results of a forecasting competition”, Journal of Forecasting, Vol. 1, 1982.

    Google Scholar 

  41. Makridakis, S., Wheelwright, S. C. and Mcgee, V. E., “Forecasting: Methods and Applications/2nd ED”, John Wiley & Sons, Inc., New York, 1983.

    Google Scholar 

  42. Powell, D., Skolnick, M. & Tong, S., “A unified approach for engineering design”, in Goonatilake, S. and Khebbal, S. (eds), Intelligent Hybrid Systems, John Wiley, Chichester, 1995.

    Google Scholar 

  43. Collopy, F. and Armstrong, J. S., “Rule-based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations”, Management Science, Vol. 38, 1992.

    Google Scholar 

  44. Ahlburg, D. A. et al, “A commentary on error measures”, International Journal of Forecasting, Vol. 8, 1992.

    Google Scholar 

  45. Liu, X. O., Ang, B. W and Goh, T. N., “Forecasting of electricity consumption”, International Joint Conference on Neural Networks, Singapore, 1991.

    Google Scholar 

  46. Lippmann, R. P, “An introduction to computing with neural nets.”, IEEE ASSP Magazine, 4, 1987.

    Google Scholar 

  47. Haykin, S., “Neural Networks: A Comprehensive Foundation,” Macmillan College Publishing Company, Inc., 1994.

    Google Scholar 

  48. Poggio, T., and Girosi, F., “Networks for Approximation and Learning.” Proceedings of the IEEE, 1990.

    Google Scholar 

  49. Broomhead, D. S., and Lowe, D. “Multivariable functional interpolation and adaptive networks.” RSRE Memorandum No. 4341. Royal Signals and Radar Establishment, Malvern, UK, 1989.

    Google Scholar 

  50. Moody, J. E., and Darken, C. J., “Fast Learning in Networks of Locally-Tuned Processing Units.” Neural Computation, 1, 1989.

    Google Scholar 

  51. Leonard, J. A., and Kramer, M. A., “Radial Basis Functions for Classifying Process Faults.” IEEE Control Systems, April, 1991.

    Google Scholar 

  52. Leonard, J. A., Kramer, M. A., and Ungar, L. H., “Using Radial Basis Functions to Approximate a Function and Its Error Bounds.” IEEE Transactions on Neural Networks, 3, 1992.

    Google Scholar 

  53. Renals, S., “Radial Basis Function Network for Speech Pattern Classification.” Electronics Letter, 25, 1989.

    Google Scholar 

  54. NeuralWare Inc., “Neural Works Professional II/Plus and NeuralWorks Explorer,” 1993.

    Google Scholar 

  55. Wack, P, “Scenarios: Shooting the Rapids”, Harvard Business Review, Vol. 62, 1985.

    Google Scholar 

  56. Linneman, R. E., and Klein, H. E., “The Use of Multiple Scenarios by U.S. Industrial Companies: A Comparison Study”, 1977–1982, Long Range Planning, Vol. December 1983.

    Google Scholar 

  57. Duval, A. et al, “Cross-Impact Analysis: A Handbook on Concepts and Applications”, Maynard M. Baldwin, Editor, Portraits of Complexity. Applications of Systems Methodologies to Societal Problems, Battelle Memorial Institute, Columbus, OH, 1975.

    Google Scholar 

  58. Okada, H. et al, Stock Market Prediction System with Modular Neural Networks”, International Joint Conference on Neural Networks, IEEE, 1990.

    Google Scholar 

  59. Higgins, C. M. and Goodman, R. M. “Incremental learning using rule-based neural networks”, Proceedings of International Joint Conference on Neural Networks, vol. 1, July 1991.

    Google Scholar 

  60. Wong, F. S., et al., “Fuzzy Neural Systems for Stock Selection”, Financial Analysts Journal, vol. 48, no. 1, Jan/Feb 1992.

    Google Scholar 

  61. Wang, P Z. and Zhang, H. M. “Truth-valued flow inference and its dynamic analysis”, Journal of Beijing Normal University (Natural Science), No. 1, 1989.

    Google Scholar 

  62. Wang, P. Z. and Zhang, H. M., “Truth-valued-flow inference (TVFI) and its applications in approximate reasoning”, Advances in Fuzzy System: Applications and Theory, World Scientific Publishing Company, 1991.

    Google Scholar 

  63. Lui, H. C., Wang, P Z. and Shen, Z., “A Fuzzy Neural System for Control applications”, Proceedings of the International Symposium on Fuzzy System, 1992.

    Google Scholar 

  64. Phillips L. D., and Beach L. R., “Special Issue on Judgmental Forecasting Preface”, Journal of Forecasting, Vol. 9, 1990.

    Google Scholar 

  65. Dalrymple D. J., “Sales forecasting practices: results from a United States survey”, International Journal of Forecasting, Vol. 10, 1990.

    Google Scholar 

  66. Armstrong, J. S., “Long-Range Forecasting: from Crystal Ball to Computer”, 2nd ed, John Wiley, New York, 1985.

    Google Scholar 

  67. Kahneman D., and Tversky A., “Intuitive Prediction: Biases and Corrective Procedures”, TIMS Studies in Management Sciences, Vol. 12, 1979.

    Google Scholar 

  68. Lawrence, M. J., Edmundson, R. H. and O’Connor, M. J., “The Accuracy of Combining Judgmental and Statistical Forecasts”, Management Science, Vol. 32, 1986.

    Google Scholar 

  69. Jenkins G., “Some Prectical aspects of forecasting in organization”, Journal oj Forecasting, Vol. 1, 1982.

    Google Scholar 

  70. Mahmoud E., “Combining Forecasts:Some Managerial Issues”, International Journal of Forecasting, Vol. 5, 1989.

    Google Scholar 

  71. Zimmerman H. J., “Fuzzy Set Theory and Its Application”, Kluwer Nijhoff, Boston, 1985.

    Google Scholar 

  72. Wang Z., “Fuzzy Set and Application (in Chinese)”, The Centre of China Productivity, Taiwan, 1990a.

    Google Scholar 

  73. Zadeh L. A., “Fuzzy Logic”, Computer, Vol. 21, No.4, 1988.

    Google Scholar 

  74. Wang P Z., and Sugeno M., “The factor fields and background structure for fuzzy subsets”, Fuzzy Mathematics, Vol. 2, 1982.

    Google Scholar 

  75. Wang P Z., “From the Fuzzy Statistics to the Falling Random Subsets”, In: P. P. Wang, ed., Advances in Fuzzy Sets, Possibility and Applications, Plenum Press, New York, 1983.

    Google Scholar 

  76. Wang P. Z., “Factor Spaces and Knowledge Representation”, Approximate Reasoning Tools for Artificial Intelligence, J. L. Verdegay and M. Delgado (eds.), Verlag, Vol. 7, 1990b.

    Google Scholar 

  77. Press H. W, Flannery B. P, Teukolsky S. A., and Vetterlin W T., “The Art of Scientific Computing (Fortran Version)”, Numerical Recipes, Cambridge University Press, 1989.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Singapore Institute of Manufacturing Technology, Singapore

    Xiang Li & Cheng-Leong Ang

  2. Institute of Communication and Information Systems School of Electrical and Electronic Engineering, Nanyang Technological University, SINGAPORE

    Robert Gay

Authors
  1. Xiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cheng-Leong Ang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Robert Gay
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of California, Los Angeles, USA

    Cornelius T. Leondes

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Kluwer Academic Publishers

About this chapter

Cite this chapter

Li, X., Ang, CL., Gay, R. (2005). An Intelligent Hybrid System for Business Forecasting. In: Leondes, C.T. (eds) Intelligent Knowledge-Based Systems. Springer, Boston, MA. https://doi.org/10.1007/978-1-4020-7829-3_35

Download citation

  • DOI: https://doi.org/10.1007/978-1-4020-7829-3_35

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4020-7746-3

  • Online ISBN: 978-1-4020-7829-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation