Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
On Similarity Queries for Time-Series Data: Constraint Specification and Implementation
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Stock time series visualization based on data point importance
Engineering Applications of Artificial Intelligence
Stock Forecast Method Based on Wavelet Modulus Maxima and Kalman Filter
ICMECG '10 Proceedings of the 2010 International Conference on Management of e-Commerce and e-Government
Fitness function evaluation for MA trading strategies based on genetic algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Forecasting Stock Exchange Movements Using Neural Networks: A Case Study
ICFCSA '11 Proceedings of the 2011 International Conference on Future Computer Sciences and Application
Stock Market Prediction Using Artificial Neural Networks Based on HLP
IHMSC '11 Proceedings of the 2011 Third International Conference on Intelligent Human-Machine Systems and Cybernetics - Volume 01
Multi-dimensional pattern discovery in financial time series using sax-ga with extended robustness
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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This paper presents a new computational finance approach, combining a Symbolic Aggregate approXimation (SAX) technique together with an optimization kernel based on genetic algorithms (GA). The SAX representation is used to describe the financial time series, so that, relevant patterns can be efficiently identified. The evolutionary optimization kernel is here used to identify the most relevant patterns and generate investment rules. The proposed approach was tested using real data from S&P500. The achieved results show that the proposed approach outperforms both B&H and other state-of-the-art solutions.