Deep Prediction of Stock Prices with K-Means Clustered Data Augmentation

Kyounghoon Han; Huigyu Yang; Hyunseung Choo

Deep Prediction of Stock Prices with K-Means Clustered Data Augmentation

Kyounghoon Han, Huigyu Yang, Hyunseung Choo, Journal of Internet Computing and Services, Vol. 24, No. 2, pp. 67-74, Apr. 2023

10.7472/jksii.2023.24.2.67, Full Text:

Keywords: Stock Price Prediction, clustering, data augmentation, Deep Learning, Artificial intelligence

Abstract

Stock price prediction research in the financial sector aims to ensure trading stability and achieve profit realization. Conventional statistical prediction techniques are not reliable for actual trading decisions due to low prediction accuracy compared to randomly predicted results. Artificial intelligence models improve accuracy by learning data characteristics and fluctuation patterns to make predictions. However, predicting stock prices using long-term time series data remains a challenging problem. This paper proposes a stable and reliable stock price prediction method using K-means clustering-based data augmentation and normalization techniques and LSTM models specialized in time series learning. This enables obtaining more accurate and reliable prediction results and pursuing high profits, as well as contributing to market stability.

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Cite this article

[APA Style]

Han, K., Yang, H., & Choo, H. (2023). Deep Prediction of Stock Prices with K-Means Clustered Data Augmentation. Journal of Internet Computing and Services, 24(2), 67-74. DOI: 10.7472/jksii.2023.24.2.67.

[IEEE Style]

K. Han, H. Yang, H. Choo, "Deep Prediction of Stock Prices with K-Means Clustered Data Augmentation," Journal of Internet Computing and Services, vol. 24, no. 2, pp. 67-74, 2023. DOI: 10.7472/jksii.2023.24.2.67.

[ACM Style]

Kyounghoon Han, Huigyu Yang, and Hyunseung Choo. 2023. Deep Prediction of Stock Prices with K-Means Clustered Data Augmentation. Journal of Internet Computing and Services, 24, 2, (2023), 67-74. DOI: 10.7472/jksii.2023.24.2.67.