Machine learning: neural networks, genetic algorithms, and fuzzy systems
Machine learning: neural networks, genetic algorithms, and fuzzy systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Design of Fully and Partially Connected Random Neural Networks for Pattern Completion
IWANN '93 Proceedings of the International Workshop on Artificial Neural Networks: New Trends in Neural Computation
A TSK type fuzzy rule based system for stock price prediction
Expert Systems with Applications: An International Journal
ICETET '08 Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 02
A neural network with a case based dynamic window for stock trading prediction
Expert Systems with Applications: An International Journal
Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network
Expert Systems with Applications: An International Journal
Games for extracting randomness
Proceedings of the 5th Symposium on Usable Privacy and Security
Application Study of BP Neural Network on Stock Market Prediction
HIS '09 Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems - Volume 03
A New Approach of Stock Price Prediction Based on Logistic Regression Model
NISS '09 Proceedings of the 2009 International Conference on New Trends in Information and Service Science
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Incorporating the Markov chain concept into fuzzy stochastic prediction of stock indexes
Applied Soft Computing
A hybrid forecast marketing timing model based on probabilistic neural network, rough set and C4.5
Expert Systems with Applications: An International Journal
A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting
Information Sciences: an International Journal
An ensemble of neural networks for stock trading decision making
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Forecasting stock exchange movements using neural networks: Empirical evidence from Kuwait
Expert Systems with Applications: An International Journal
Forecasting trends of high-frequency KOSPI200 index data using learning classifiers
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
This paper proposes a novel model by evolving partially connected neural networks (EPCNNs) to predict the stock price trend using technical indicators as inputs. The proposed architecture has provided some new features different from the features of artificial neural networks: (1) connection between neurons is random; (2) there can be more than one hidden layer; (3) evolutionary algorithm is employed to improve the learning algorithm and training weights. In order to improve the expressive ability of neural networks, EPCNN utilizes random connection between neurons and more hidden layers to learn the knowledge stored within the historic time series data. The genetically evolved weights mitigate the well-known limitations of gradient descent algorithm. In addition, the activation function is defined using sin(x) function instead of sigmoid function. Three experiments were conducted which are explained as follows. In the first experiment, we compared the predicted value of the trained EPCNN model with the actual value to evaluate the prediction accuracy of the model. Second experiment studied the over fitting problem which occurred in neural network training by taking different number of neurons and layers. The third experiment compared the performance of the proposed EPCNN model with other models like BPN, TSK fuzzy system, multiple regression analysis and showed that EPCNN can provide a very accurate prediction of the stock price index for most of the data. Therefore, it is a very promising tool in forecasting of the financial time series data.