scroll to top
0

Mobile Menu

Header Layout

EBSCO Auth Banner

Let's find your institution. Click here.

Page title

LSTM을 이용한 주가예측 모델의 학습방법에 따른 성능분석.

  • Academic Journal
  • 정종진1 jjjung@daejin.ac.kr
    김지연2 jykim629@daejin.ac.kr
  • Journal of Digital Convergence. 2020, Vol. 18 Issue 11, p259-266. 8p.
  • Many developments have been steadily carried out by researchers with applying knowledge-based expert system or machine learning algorithms to the financial field. In particular, it is now common to perform knowledge based system trading in using stock prices. Recently, deep learning technologies have been applied to real fields of stock trading marketplace as GPU performance and large scaled data have been supported enough. Especially, LSTM has been tried to apply to stock price prediction because of its compatibility for time series data. In this paper, we implement stock price prediction using LSTM. In modeling of LSTM, we propose a fitness combination of model parameters and activation functions for best performance. Specifically, we propose suitable selection methods of initializers of weights and bias, regularizers to avoid over-fitting, activation functions and optimization methods. We also compare model performances according to the different selections of the above important modeling considering factors on the real-world stock price data of global major companies. Finally, our experimental work brings a fitness method of applying LSTM model to stock price prediction. [ABSTRACT FROM AUTHOR]
Additional Information
A Performance Analysis by Adjusting Learning Methods in Stock Price Prediction Model Using LSTM.
Copyright of Journal of Digital Convergence is the property of Society of Digital Policy & Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

banner_970x250 (970x250)

sponsored