Machine Learning Algorithms in Stock Market Prediction

被引：0

作者：

Potdar, Jayesh ^{[1
]}

Mathew, Rejo ^{[1
]}

机构：

[1] NMIMS Deemed Be Univ, Mukesh Patel Sch Technol Management & Engn, Dept IT, Mumbai, Maharashtra, India

来源：

PROCEEDING OF THE INTERNATIONAL CONFERENCE ON COMPUTER NETWORKS, BIG DATA AND IOT (ICCBI-2018) | 2020年 / 31卷

关键词：

Stock market; Predictive model; Machine learning; Neural network; Support vector machine; PSO-Elman Neural Network; Levenberg Marquardt algorithm; Support Vector Regression; Linear regression;

D O I：

10.1007/978-3-030-24643-3_23

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Fundamentally, stock markets stand for an undeviating, nonparametric system accompanied with undetermined disturbances due to which the accurate prediction of the stock price becomes challenging. Stock prediction is achievable with the help of technology assisted practises. The objective of this review paper is to compare machine learning algorithms namely Support Vector Regression (SVR), Improved Levenberg Marquardt Algorithm (LMA) Self-adapting Variant PSO-Elman Neural Network (PSO) and Linear Regression (LR) so as to have a precise price trend.

引用

页码：192 / 197

页数：6

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