Abstract
Stock trend prediction has received a significant amount of attention in recent years. Existing methods could not exploit the peculiar trends for prediction, which are valuable in rising-falling trend analysis for short-term or long-term investments. In this paper, we propose an integrated model that can discover peculiar trend patterns for stock trend prediction. Our proposed model is mainly divided into two parts: the clustering and prediction processes. In the clustering process, we use a Graph Convolutional Network (GCN) model to explore the trend patterns groups from a set of subsequences of time series. In the prediction process, an Long Short-term Memory (LSTM) model will be trained based on the discovered patterns for predicting future stock trends. Experimental results on real-world financial datasets demonstrate that our model yields better performance in terms of stock trend prediction, and outperforms state-of-the-art forecasting models for long-term investment.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abonyi, J., Feil, B., Nemeth, S., Arva, P.: Modified gath-geva clustering for fuzzy segmentation of multivariate time-series. Fuzzy Sets Syst. 149, 39–56 (2005)
Alfke, D., Stoll, M.: Semi-supervised classification on non-sparse graphs using low-rank graph convolutional networks. arXiv preprint 1905, 10224 (2019)
Birant, D., Kut, A.: ST-DBSCAN: an algorithm for clustering spatial-temporal data. Data Knowl. Eng. 60(1), 208–221 (2007)
Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)
Chen, M.Y., Liao, C.H., Hsieh, R.P.: Modeling public mood and emotion: stock market trend prediction with anticipatory computing approach. computers in human behavior. Comput. Human Behav. 101, 402–408 (2019)
Chen, W., Jiang, M., Zhang, W.G., Chen, Z.: A novel graph convolutional feature based convolutional neural network for stock trend prediction. Inform. Sci. 556, 67–94 (2021)
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. NIPS 29 (2016)
Fu, S., Liu, W., Tao, D., Zhou, Y., Nie, L.: Hesgcn: Hessian graph convolutional networks for semi-supervised classification. Inform. Sci. 514, 484–498 (2020)
Guan, X., Huang, C., Liu, G., Meng, X., Liu, Q.: Mapping rice cropping systems in vietnam using an NDVI-based time-series similarity measurement based on DTW distance. Remote Sensing 8, 19 (2016)
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems 30 (2017)
Hao, Y., Gao, Q.: Predicting the trend of stock market index using the hybrid neural network based on multiple time scale feature learning. Appl. Sci. 10, 3961 (2020)
Idrees, S.M., Alam, M.A., Agarwal, P.: A prediction approach for stock market volatility based on time series data. IEEE Access 7, 17287–17298 (2019)
Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144 (2016)
Johansen, A., Sornette, D.: Evaluation of the quantitative prediction of a trend reversal on the japanese stock market in 1999. Int. J. Modern Phys. (C) 11, 359–364 (2000)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Lee, M.C.: Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Syst. Appl. 36, 10896–10904 (2009)
Li, R., Wang, S., Zhu, F., Huang, J.: Adaptive graph convolutional neural networks. In: AAAI. vol. 32 (2018)
Li, S., Li, W.T., Wang, W.: Co-gcn for multi-view semi-supervised learning. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 4691–4698 (2020)
Liang, Q., Rong, W., Zhang, J., Liu, J., Xiong, Z.: Restricted boltzmann machine based stock market trend prediction. In: IJCNN, pp. 1380–1387 (2017)
Lin, T., Guo, T., Aberer, K.: Hybrid neural networks for learning the trend in time series. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pp. 2273–2279. No. CONF (2017)
Lin, Y., Meng, Y., Sun, X., Han, Q., Kuang, K., Li, J., Wu, F.: Bertgcn: Transductive text classification by combining gcn and bert. arXiv preprint arXiv:2105.05727 (2021)
Liu, Z., Shen, Z., Li, S., Helwegen, K., Huang, D., Cheng, K.T.: How do adam and training strategies help bnns optimization. In: International Conference on Machine Learning, pp. 6936–6946. PMLR (2021)
Livieris, I.E., Pintelas, E., Pintelas, P.: A CNN-LSTM model for gold price time-series forecasting. Neural Comput. Appl. 32, 17351–17360 (2020)
Paparrizos, J., Gravano, L.: k-shape: Efficient and accurate clustering of time series. In: SIGMOD, pp. 1855–1870 (2015)
Rini, D., Novianti, P., Fransiska, H.: Internal cluster validation on earthquake data in the province of bengkulu. In: IOP Conference Series: Materials Science and Engineering, vol. 335, p. 012048. IOP Publishing (2018)
Roondiwala, M., Patel, H., Varma, S.: Predicting stock prices using LSTM. IJSR 6, 1754–1756 (2017)
Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Appl. Soft Comput. 70, 525–538 (2018)
Shephard, N., Pitt, M.K.: Likelihood analysis of non-gaussian measurement time series. Biometrika 84, 653–667 (1997)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Wang, Z., Zheng, L., Li, Y., Wang, S.: Linkage based face clustering via graph convolution network. In: CVPR, pp. 1117–1125 (2019)
Yao, S., Luo, L., Peng, H.: High-frequency stock trend forecast using LSTM model. In: ICCSE, pp. 1–4 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sun, M., Chen, R., Zhang, J., Wang, S. (2023). Unsupervised Learning via Graph Convolutional Network for Stock Trend Prediction. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2023. Lecture Notes in Networks and Systems, vol 661. Springer, Cham. https://doi.org/10.1007/978-3-031-29056-5_32
Download citation
DOI: https://doi.org/10.1007/978-3-031-29056-5_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-29055-8
Online ISBN: 978-3-031-29056-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)