research-article

Effects of Activation Functions and Optimizers on Stock Price Prediction using LSTM Recurrent Networks

Authors:

Masud Rana,

Md. Mohsin Uddin,

Md. Mohaimnul HoqueAuthors Info & Claims

CSAI '19: Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence

Pages 354 - 358

https://doi.org/10.1145/3374587.3374622

Published: 04 March 2020 Publication History

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Abstract

In stock exchange market, investors need to decide which shares to buy based on their future market value. Because of the variable market, it is obligatory to have a reliable prediction of the values of the stocks. Now-a-days the machine learning system can forecast well than the contemporary stock prediction methods. Machine learning system provides a scientific demonstration based on sample data to forecast. In this work, Linear Regression (LR), Support Vector Regression (SVR) and, Long Short-Term Memory (LSTM) algorithms are used to predict stock market prediction. Among the several features, the most important feature has been selected by using the feature selection algorithm, which is closing price. The effects of different activation functions and optimizers are experimented on stock price prediction using LSTM networks.

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Index Terms

Effects of Activation Functions and Optimizers on Stock Price Prediction using LSTM Recurrent Networks
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

CSAI '19: Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence

December 2019

370 pages

ISBN:9781450376273

DOI:10.1145/3374587

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Shenzhen University: Shenzhen University

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Association for Computing Machinery

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Published: 04 March 2020

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CSAI2019: 2019 3rd International Conference on Computer Science and Artificial Intelligence

December 6 - 8, 2019

IL, Normal, USA

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