A Modified BPN Approach For Stock Market Prediction

被引：0

作者：

Mithani, Fesal ^{[1
]}

Machchhar, Sahista ^{[1
]}

Jasdanwala, Fernaz ^{[1
]}

机构：

[1] MEF Grp Inst, Fac PG Studies, Dept Comp Engn, Rajkot 360003, Gujarat, India

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMPUTING RESEARCH | 2016年

关键词：

BPN (Back Propagation Neural) Network; Regression; ANN(Artificial Neural network); BN(Bayesian Network); SVM(Support vector machine); Accuracy;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Predicting stock market accurately has always fascinated the market analysts. During the previous few decades assorted machine learning techniques (Regression, RBFN, SOM, BN and SVM) have been applied to examine the highly debatable nature of stock market by capturing and using repetitive patterns. Our main aim is to accurately predict value for the future and maximum amount of profit for a holder by using Back Propagation Neural Network. Here we gives brief information about assorted techniques used for prediction, so that it is easy for user to choose the share to buy or sale and predict the value which has minimum error.

引用

页码：902 / 905

页数：4

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