Abstract
Generally, a neural network spends much computation time and cost in forecasting the value and movement of a stock. The reason is because a neural network requires exponential time in computation according to the number of units in a hidden layer.
The objective of the paper is to optimally build a neural network through structurally learning. The results enable us to reduce the computational time and cost as well as to understand the structure more easily.
In the paper the method is employed in forecasting the price movement of a stock. The optimization of the network by the structured learning is evaluated based on its real use.
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References
Kutsurelis, J.E.: Forecasting financial markets using neural networks: An analysis of methods and accuracy. Naval Post Graduate School (1998)
Elman, J.L.: Finding Structure in Time, Tech. Report CRL-8801, University of California, San Diego (1988)
Jordan, M.I.: Serial order: A parallel, distributed processing approach, Tech. Report ICS-8604, University of California, San Diego (1986)
Hayashi, Y.: Stock Price Is Forecastable - Introduction to Chart Analysis. Kanki Publishing (2000)
Hagiwara, M.: Buck-Propagation with Selection Mechanism, Report of Institute of Electronics, Information, NC89-104 (1989)
Asahi, T., Murakami, K., Sagamihara, T.: BP Algorithm for Optimality and highly speeding of Neural Network Structural Learning, Report of Institute of Electronics, Information, NC90-64 (1991)
Ishikawa, M.: Recent Topics on Learning Algorithms of Neural Network. Instrument and Control 30(4), 285–290 (1991)
Matsunaga, Y., Nakade, Y., Nakagawa, O., Murase, K.: Buck Propagation Algorithm to Auto-Elimitation of hidden units in Higher Neural Network. Trans. of Institute of Electronics, Information J74-D-II(8), 1118–1121 (1991)
Matsunaga, Y., Nakade, Y., Nakagawa, O., Kawanaka, M.: Buck Propagation Algorithm to auto eliminate redundant hidden units from Competition. Trans. of Institute of Electronics, Information J79-D-II(3), 403–412 (1996)
Murata, I., Watada, J., Choi, J.: Structural Learning of Neural Network and Its Application to Stock Forecasting. In: Proceedings, International Conference on Management Engineering (ISME 2006), Kitakyushu, JAPAN, March 10-12, 2006, pp. R30/01–R30/06 (2006)
Watada, J.: Structural Learning o Neural Network and Its Application. In: Keynote Speech. Proceedings, An International Conference on Intelligent systems (ICIS 2005), Kuala Lumpur, Malaysia, December 1-3, 2005, pp. 4–12 (2005)
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Watada, J. (2006). Structural Learning of Neural Networks for Forecasting Stock Prices. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893011_123
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DOI: https://doi.org/10.1007/11893011_123
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46542-3
Online ISBN: 978-3-540-46544-7
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