Comparative study of an intelligent dynamic approaches in predicting exchange rate

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@InProceedings{Indrakala:2016:ICETETS,

• author = "S. Indrakala and T. Chitrakalarani",
• booktitle = "2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS)",
• title = "Comparative study of an intelligent dynamic approaches in predicting exchange rate",
• year = "2016",
• abstract = "The objective of the projected paper is to do study, development in an intelligent dynamic methods to expect the financial goods. For financial shop expectation different methods like Rough Set, Genetic Programming with Boosting Technique, Best Replacement Optimisation (BRO), and Genetic Programming with Rough Set and BRO with Rough Set are used. These models tested with five datasets representing different sectors in S&P 50 stock market and used to predict daily stock prices. Results presented in this paper showed that the proposed BRO-RS model have quick convergence rate at early stages of the iterations. BRO-RS model achieved better accuracy than compared models in price and trend prediction.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ICETETS.2016.7603125",
• month = feb,
• notes = "Also known as \cite{7603125}",

}

Genetic Programming entries for S Indrakala T Chitrakalarani

Citations