Neural Networks in Business Forecasting
Keywords:
Forecasting, Neural Networks, BusinessAbstract
Neural Network is defined as the ability of a group to solve more problems than its individual members. The idea brings that a group of people can solve problems efficiently and offer greater insight and a better answer than any one individual could provide. The applications of Neural Network enhance an innovative business model for an enterprise. Role of Neural Network in an enterprise brings effectiveness. Further work will be carried out towards the Mathematical modeling of neural networks and various parameters will be engaged so as to get the required result to desired degree of accuracy [1].
References
Armstrong, J.S., Long-Range Forecasting, Second Edition, New York: Wiley, 1985.
Bell, T., G. Ribar, and J. Verchio, "Neural Nets vs. Logistic Regression," presented at the University of Southern California Expert Systems Symposium, November, 1989.
Chatfield, C., "Neural Networks: Forecasting Breakthrough or Passing Fad?," International Journal of Forecasting, 1993, 9, 1-3.
Collopy, F. and J.S. Armstrong, "Expert Opinions about Extrapolation and the Mystery of the Overlooked Discontinuities," International Journal of Forecasting, 1992, 8, 575-582.
Connor, D., "Data Transformation Explains the Basics of Neural Networks," EDN, May 12, 1988, 138-144.
Cybenko, G., "Continuous Valued Neural Networks with Two Hidden Layers Are Sufficient," Technical Report, Department of Computer Science, Tufts University, 1988.
Dalrymple, D.J., "Sales Forecasting Practices: Results of a United States Survey," International Journal of Forecasting, 1987, 3, 379-392.
De Gooijer, I.G. and K. Kumar, "Some Recent Developments in NonLinear Modelling, Testing, and Forecasting," International Journal of Forecasting, 1992, 8, 135-156.
Donaldson, R.G., M. Kamstra, and H.Y. Kim, "Evaluating Alternative Models for Conditional Stock Volatility: Evidence from International Data," Working Paper, University of British Columbia, 1993.
Duliba, K., "Contrasting Neural Nets with Regression in Predicting Performance," Proceedings of the 24th Hawaii International Conference on System Sciences, 1991, Vol. 4, 163-170.
Dutta, S. and S. Shekhar, "Bond Rating: A Non-Conservative Application of Neural Networks," Proceedings of the 1988 International Conference on Neural Networks, 1988, Vol. 2, 443450.
Foster, B., F. Collopy, and L. Ungar, "Neural Network Forecasting of Short, Noisy Time Series," presented at the ORSA TIMS National Meeting, May, 1991.
Gluck, M.A. and G.H. Bower, "Evaluating an Adaptive Model of Human Learning," Journal of Memory and Language, 1989, 27, 166-195.
Gorr, W.I., D. Nagin, and J. Szczypula, "Comparative Study of Artificial Neural Networks and Statistical Methods for Predicting Student Grade Point Averages, " Working Paper, Carnegie Mellon University, 1993.
Granger, R., J. Ambros-Ingerson, U. Staubli, and G. Lynch, "Memorial Operation of Multiple, Interacting Simulated Brain Structures," in Neuroscience and Connectionist Models, M. Gluck and D. Rumelhart, eds., Hillsdale: Erlbaum Associates, 1989.
Hiew, M. and G. Green, "Beyond Statistics. A Forecasting System That Learns," The Forum, 1992, Vol. 5, pp. 1 and 6.
Hill, T., M. O'Connor, and W. Remus, "Neural Networks for Time Series Forecasting," Working Paper, University of Hawaii, 1993, Third round at Management Science.
Hill, T. and W. Remus, "Neural Network Models for Intelligent Support of Managerial Decision Making," Decision Support Systems, 1993, Forthcoming.
Hoptroff, R.G., "The Principles and Practice of Time Series Forecasting and Business Modeling Using Neural Nets," Neural Computing & Applications, 1993, 1, 59-66.
Hornik, K., M. Stinchcombe, and H. White, "Multilayer Feedforward Networks are Universal Approximators," Neural Networks, 1989, 2(5), 359-366.
Kang, S., An Investigation of the Use of Feedforward Neural Networks for Forecasting, Ph.D. Dissertation, Kent State, 1991.
Koster, A., N. Sondak, and W. Bourbia, "A Business Application of Artificial Neural Network Systems," The Journal of Computer Information Systems, 1990, Vol. XI, 3-10.
Lawrence, M.J., R.H. Edmundson, and M.J. O'Connor, "An Examination of the Accuracy of Judgemental Extrapolation of Time Series," International Journal of Forecasting, 1985, 1, 25-35.
Makridakis, S., A. Anderson, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, and R. Winkler, "The Accuracy of Extrapolation (Time Series) Methods: Results of a Forecasting Competition," Journal of Forecasting, 1982, 1, 111-153.
Makridakis, S. and R. L. Winkler, "Averages of Forecasts: Some Empirical Results," Management Science, 1985, 29, 987-996.
Marquez, L., Function Approximation Using Neural Networks: A Simulation Study, Ph.D. Dissertation, University of Hawaii, 1992.
Marquez, L., T. Hill, W. Remus, and R. Worthley, "Neural Network Models as an Alternative to Regression," in Neural Network Applications in Finance and Investing edited by R. Trippi and E. Turban, Chicago: Probus Publishing, 1992, 435-450.
Nelder, J. and R. Mead, "The Downhill Simplex Method," Computer Journal, 1965, 7, 308-310.
Odom, M. and R. Sharda, "A Neural Network Model for Bankruptcy Prediction," Proceedings of the 1990 International Joint Conference on Neural Networks, 1990, Vol. 2, 163-168.
Pack, D.J. and D.J. Downing, "Why Didn't Box-Jenkins Win (Again)?" presented at the Third International Symposium on Forecasting, 1983.
Raghupathi, W., L. Schkade, and R. Bapi, "A Neural Network Application for Bankruptcy Prediction," Proceedings of the 24th Hawaii International Conference on System Sciences, 1991, Vol. 4, 147-155.
Remus, W., "A Study of the Impact of Graphical and Tabular Displays and Their Interaction with Environmental Complexity," Management Science, 1987, Vol. 33, 1200-1205.
Remus, W. and T. Hill, "Neural Network Models of Managerial Judgment," Proceedings of the 23rd Hawaii International Conference on System Sciences, 1990, Vol. 4, 340-344. Forthcoming in Advances in Artificial Intelligence in Business and Finance.
Roy, J. and J. Cosset, "Forecasting Country Risk Ratings Using a Neural Network," Proceedings of the 23rd Hawaii International Conference on System Sciences, 1990, Vol. 4, 327-334.
Rumelhart, D. and J. McClelland, Parallel Distributed Processing, Cambridge: MIT Press, 1986.
Rumelhart, D., G.E. Hinton, and R.J. Williams, "Learning Representations by Back-Propagating Errors," Nature, 1986, 323, 533-536.
Sharda, R. and R. Patil, "Neural Networks as Forecasting Experts: An Empirical Test," Proceedings of the 1990 International Joint Conference on Neural Networks Meeting, 1990, Vol.
Sharda, R. and R. Patil, "Connectionist Approach to Time Series Prediction: An Empirical Test," Journal of Intelligent Manufacturing, 1992, Forthcoming.
Sietsma, J. and R. Dow, "Creating Artificial Neural Networks That Generalize," Neural Networks, 1991, 4, 67-79.
Surkan, A. and J. Singleton, "Neural Networks for Bond Rating Improved by Multiple Hidden Layers," Proceedings of the 1990 International Joint Conference on Neural Networks, 1990, Vol. 2, 157-162.
Tam, K.Y., "Neural Network Models and the Prediction of Bank Bankruptcy," Omega, The International Journal of Management Science, 1991, 19, 429-445.
Tam, K.Y. and M.Y. Kiang, "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, 1992, 38, 926-947.
Tang, Z., C. de Almeida, and P. Fishwick, "Time Series Forecasting Using Neural Networks vs. Box-Jenkins Methodology," Simulation, 1991, Vol. 57, 303-310.
Wasserman, P.D., Neural Computing: Theory and Practice, Van Nostrand Reinhold: New York, 1989.
Weigend, A., B. Huberman, and D. Rumelhart, "Predicting the Future: A Connectionist Approach," International Journal of Neural Systems, 1990, 1, 193-209.
Widrow, B. and S.D. Sterns, Adaptive Signal Processing, Englewood Cliffs, NJ: Prentice-Hall, 1985.
Yoon, Y. and G. Swales, "Predicting Stock Price Performance," Proceedings of the 24th Hawaii International Conference on System Sciences, 1991, Vol. 4, 156-162.
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