This paper is published in Volume-5, Issue-3, 2019
Area
Machine Learning
Author
Aiswarya S. Kumar
Co-authors
Greeshma Merin Varghese, Radhu Krishna R., Reshma K. Pillai
Org/Univ
College of Engineering, Chengannur, Kerala, India
Pub. Date
11 May, 2019
Paper ID
V5I3-1284
Publisher
Keywords
Stock prediction, Linear Regression, Fuzzification, SVM

Citationsacebook

IEEE
Aiswarya S. Kumar, Greeshma Merin Varghese, Radhu Krishna R., Reshma K. Pillai. A review on stock prediction using machine learning, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Aiswarya S. Kumar, Greeshma Merin Varghese, Radhu Krishna R., Reshma K. Pillai (2019). A review on stock prediction using machine learning. International Journal of Advance Research, Ideas and Innovations in Technology, 5(3) www.IJARIIT.com.

MLA
Aiswarya S. Kumar, Greeshma Merin Varghese, Radhu Krishna R., Reshma K. Pillai. "A review on stock prediction using machine learning." International Journal of Advance Research, Ideas and Innovations in Technology 5.3 (2019). www.IJARIIT.com.

Abstract

The goal of this review is to describe the various methods used to predict the stock market, gold price and fuel price. The following paper describes the work that was done on investigating the application of regression, SVM, ELM, ANFIS techniques on the stock market price prediction. The report describes the various technologies with their accuracy level and efficiency in the test phase. It was found that support vector regression was the most effective out of the models used, although there are opportunities to expand this research further using additional techniques to incorporate the current affairs into the prediction features.
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