TY - GEN
T1 - Structural learning of neural networks for forecasting stock prices
AU - Watada, Junzo
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
KW - Forecasting the stock
KW - Structural learning
UR - http://www.scopus.com/inward/record.url?scp=33750726471&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33750726471&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:33750726471
SN - 3540465421
SN - 9783540465423
VL - 4253 LNAI - III
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 972
EP - 979
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2006
Y2 - 9 October 2006 through 11 October 2006
ER -