Comparative study of an intelligent dynamic approaches in predicting exchange rate
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @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