Stock Price Prediction Methodology Using Shallow Convolutional Neural Network 


Vol. 48,  No. 6, pp. 751-754, Jun.  2023
10.7840/kics.2023.48.6.751


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  Abstract

Recently, research on analyzing and forecasting time series data using deep learning has been actively conducted. These time series forecasting using deep learning models learn the characteristics of time series such as trends and seasonality, and use them to predict future values. However, it is much more difficult to identify such characteristics in short-term stock price time series spanning 1 or 2 days. Therefore, it is difficult to predict stock prices using existing time series forecasting deep learning models. In this paper, we propose a methodology for forecasting daily stock trading distributions by training the pattern of stock price time series using shallow convolutional neural network (CNN). Although there are various ways to represent daily stock trading distributions, this paper proposes a methodology for forecasting box plots.

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[IEEE Style]

Y. Cho, E. Kim, H. Shin, Y. Choi, "Stock Price Prediction Methodology Using Shallow Convolutional Neural Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 6, pp. 751-754, 2023. DOI: 10.7840/kics.2023.48.6.751.

[ACM Style]

Young-Jin Cho, Eui-Yeon Kim, Hong-Gi Shin, and Yong-Hoon Choi. 2023. Stock Price Prediction Methodology Using Shallow Convolutional Neural Network. The Journal of Korean Institute of Communications and Information Sciences, 48, 6, (2023), 751-754. DOI: 10.7840/kics.2023.48.6.751.

[KICS Style]

Young-Jin Cho, Eui-Yeon Kim, Hong-Gi Shin, Yong-Hoon Choi, "Stock Price Prediction Methodology Using Shallow Convolutional Neural Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 6, pp. 751-754, 6. 2023. (https://doi.org/10.7840/kics.2023.48.6.751)
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