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Forecasting Chaotic Stock Market Data using Time Series Data Mining

by Mohammad Rafiuzzaman
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 101 - Number 10
Year of Publication: 2014
Authors: Mohammad Rafiuzzaman
10.5120/17725-8169

Mohammad Rafiuzzaman . Forecasting Chaotic Stock Market Data using Time Series Data Mining. International Journal of Computer Applications. 101, 10 ( September 2014), 27-34. DOI=10.5120/17725-8169

@article{ 10.5120/17725-8169,
author = { Mohammad Rafiuzzaman },
title = { Forecasting Chaotic Stock Market Data using Time Series Data Mining },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 101 },
number = { 10 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 27-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume101/number10/17725-8169/ },
doi = { 10.5120/17725-8169 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:31:20.348484+05:30
%A Mohammad Rafiuzzaman
%T Forecasting Chaotic Stock Market Data using Time Series Data Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 101
%N 10
%P 27-34
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An important financial subject that has attracted researchers' attention for many years is forecasting stock return. Many researchers have contributed in this area of chaotic forecast in their ways. Among them data mining techniques have been successfully shown to generate high forecasting accuracy of stock price movement. Nowadays, instead of a single aspects of stock market, traders need to use various aspects' forecasting to gain multiple signals and more information about the future of the markets. Aspects of Lyapunov, Entropy and Variance (ALEV) provide an approach for mining large stocks of time series data. This paper proposes a novel method for forecasting chaotic behavior of stock market's opening, high, low and closing price with time series data mining. The outcome of this study tries to help the investors in the stock market to decide the better timing for buying or selling stocks based on the knowledge extracted from the historical prices of such stocks.

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Index Terms

Computer Science
Information Sciences

Keywords

Stock market data mining chaos data data forecasting.

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