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Reseach Article

A GA-optimized SAX- ANN based Stock Level Prediction System

by Binoy B. Nair, Nikhil Xavier, V.p Mohandas, Adarsh Sathyapal, Anusree E G, Pawan Kumar, Vignesh Ravikumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 106 - Number 15
Year of Publication: 2014
Authors: Binoy B. Nair, Nikhil Xavier, V.p Mohandas, Adarsh Sathyapal, Anusree E G, Pawan Kumar, Vignesh Ravikumar
10.5120/18594-9840

Binoy B. Nair, Nikhil Xavier, V.p Mohandas, Adarsh Sathyapal, Anusree E G, Pawan Kumar, Vignesh Ravikumar . A GA-optimized SAX- ANN based Stock Level Prediction System. International Journal of Computer Applications. 106, 15 ( November 2014), 7-12. DOI=10.5120/18594-9840

@article{ 10.5120/18594-9840,
author = { Binoy B. Nair, Nikhil Xavier, V.p Mohandas, Adarsh Sathyapal, Anusree E G, Pawan Kumar, Vignesh Ravikumar },
title = { A GA-optimized SAX- ANN based Stock Level Prediction System },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 106 },
number = { 15 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 7-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume106/number15/18594-9840/ },
doi = { 10.5120/18594-9840 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:39:27.375036+05:30
%A Binoy B. Nair
%A Nikhil Xavier
%A V.p Mohandas
%A Adarsh Sathyapal
%A Anusree E G
%A Pawan Kumar
%A Vignesh Ravikumar
%T A GA-optimized SAX- ANN based Stock Level Prediction System
%J International Journal of Computer Applications
%@ 0975-8887
%V 106
%N 15
%P 7-12
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Forecasting stock price movements is of immense importance to any stock trader. However, traditionally, this has been accomplished using technical analysis tools. In this study, an attempt has been made to employ data mining to identify the one-day-ahead stock price levels. Two different approaches are considered. The two approaches are empirically validated on twelve stock price datasets, with the stocks drawn from the Indian, US and UK stock markets. Results indicate that both the approaches proposed in the present study are capable of successfully forecasting the one-day-ahead stock price levels.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Stock prediction Artificial Neural Network Genetic Algorithms Wavelet Symbolic Aggregate Approximation