Author(s):
N Srinivasan, C Lakshmi
Email(s):
srinijyothish@gmail.com
DOI:
10.5958/0974-360X.2017.00021.X 
Address:
N Srinivasan1*, C Lakshmi2
1Research Scholar, School of Computing, Sathyabama University, Chennai, Tamil Nadu, India.
2Professor, Department of Software Engineering, SRM University, Chennai, Tamil Nadu, India.
*Corresponding Author
Published In:
Volume - 10,
Issue - 1,
Year - 2017
ABSTRACT:
Securities trade data is a high dimensional time course of action cash related data that positions exceptional computational challenges. Stock data is variable with respect to time, suspecting the future example of the expenses is a trying task. The segments that effect the consistency of stock data can't be judged as the same variables may affect the estimation of the stock always. We propose a data burrowing approach for the desire of the advancement of securities trade. It consolidates using the innate estimation for pre taking care of and a cross breed packing strategy of Hierarchical gathering and Fuzzy C-Means for clustering. The genetic figuring helps in dimensionality diminish and packing makes highlight vectors that help with estimate.
Cite this article:
N Srinivasan, C Lakshmi. Stock Price Prediction using Rule Based Genetic Algorithm Approach . Research J. Pharm. and Tech. 2017; 10(1): 87-90. doi: 10.5958/0974-360X.2017.00021.X
Cite(Electronic):
N Srinivasan, C Lakshmi. Stock Price Prediction using Rule Based Genetic Algorithm Approach . Research J. Pharm. and Tech. 2017; 10(1): 87-90. doi: 10.5958/0974-360X.2017.00021.X Available on: https://www.rjptonline.org/AbstractView.aspx?PID=2017-10-1-21