Surveying stock market forecasting techniques–Part II: Soft computing methods
GS Atsalakis, KP Valavanis - Expert Systems with applications, 2009 - Elsevier
The key to successful stock market forecasting is achieving best results with minimum
required input data. Given stock market model uncertainty, soft computing techniques are …
required input data. Given stock market model uncertainty, soft computing techniques are …
An empirical methodology for developing stockmarket trading systems using artificial neural networks
B Vanstone, G Finnie - Expert systems with Applications, 2009 - Elsevier
A great deal of work has been published over the past decade on the application of neural
networks to stockmarket trading. Individual researchers have developed their own …
networks to stockmarket trading. Individual researchers have developed their own …
Efficient prediction of exchange rates with low complexity artificial neural network models
In recent years forecasting of financial data such as interest rate, exchange rate, stock
market and bankruptcy has been observed to be a potential field of research due to its …
market and bankruptcy has been observed to be a potential field of research due to its …
Diversity of ability and cognitive style for group decision processes
This research investigates the potential for two forms of error diversity (ability diversity and
diversity of cognitive style) to increase the accuracy of multi-agent group decision processes …
diversity of cognitive style) to increase the accuracy of multi-agent group decision processes …
Intrusion detection using k-Nearest Neighbor
M Govindarajan… - 2009 First International …, 2009 - ieeexplore.ieee.org
Data mining is the use of algorithms to extract the information and patterns derived by the
knowledge discovery in databases process. Classification maps data into predefined groups …
knowledge discovery in databases process. Classification maps data into predefined groups …
Time series prediction using dynamic ridge polynomial neural networks
Novel higher order polynomial neural network architecture is presented in this paper. The
new proposed neural network is called dynamic ridge polynomial neural network that …
new proposed neural network is called dynamic ridge polynomial neural network that …
Combining seasonal time series ARIMA method and neural networks with genetic algorithms for predicting the production value of the mechanical industry in Taiwan
YH Liang - Neural Computing and Applications, 2009 - Springer
Supplying industrial firms with an accurate method of forecasting the production value of the
mechanical industry to facilitate decision makers in precise planning is highly desirable …
mechanical industry to facilitate decision makers in precise planning is highly desirable …
Seize the (intra) day: Features selection and rules extraction for tradings on high-frequency data
M Resta - Neurocomputing, 2009 - Elsevier
We describe a method to develop trading rules based on the responses of self-organizing
maps (SOMs), trained under various distance metrics. The effectiveness of the procedure is …
maps (SOMs), trained under various distance metrics. The effectiveness of the procedure is …
A biometric templates secure transmission method based on bi-layer watermarking and pki
CL Li, YH Wang, LN Liu - 2009 International Conference on …, 2009 - ieeexplore.ieee.org
In this paper, a novel framework combining bi-layer watermarking and PKI for biometric
templates secure transmission is proposed. We design a novel bi-layer watermarking …
templates secure transmission is proposed. We design a novel bi-layer watermarking …
Artificial higher order pipeline recurrent neural networks for financial time series prediction
The research described in this chapter is concerned with the development of a novel
artificial higher order neural networks architecture called the second-order pipeline recurrent …
artificial higher order neural networks architecture called the second-order pipeline recurrent …