Genetic Programming for Financial Time Series Prediction
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
2005 Special issue: Recursive principal components analysis
Neural Networks - Special issue on neural networks and kernel methods for structured domains
Time series prediction with single multiplicative neuron model
Applied Soft Computing
Hypernetworks: A Molecular Evolutionary Architecture for Cognitive Learning and Memory
IEEE Computational Intelligence Magazine
An evolutionary approach to pattern-based time series segmentation
IEEE Transactions on Evolutionary Computation
Evolutionary hypernetworks for learning to generate music from examples
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
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The paper proposes a hypernetwork-based method for stock market prediction through a binary time series problem. Hypernetworks are a random hypergraph structure of higher-order probabilistic relations of data. The problem we tackle concerns the prediction of price movements (up/down) on stock markets. Compared to previous approaches, the proposed method discovers a large population of variable subpatterns, i.e. local and global patterns, using a novel evolutionary hypernetwork. An output is obtained from combining these patterns. In the paper, we describe two methods for assessing the prediction quality of the hypernetwork approach. Applied to the Dow Jones Industrial Average Index and the Korea Composite Stock Price Index data, the experimental results show that the proposed method effectively learns and predicts the time series information. In particular, the hypernetwork approach outperforms other machine learning methods such as support vector machines, naive Bayes, multilayer perceptrons, and k-nearest neighbors.