Stock Price Prediction Using TCN-GAN Hybrid Model
DOI:
10.33395/sinkron.v9i1.14246Keywords:
Generative Adversarial Network, ITMG, Stock Price Prediction, Temporal Convolutional NetworkAbstract
The stock market plays a vital role in national economies, offering significant profit opportunities for investors while exposing them to substantial risks due to market uncertainties. Stock prices often experience significant fluctuations, making accurate prediction a challenging task. Temporal Convolutional Network (TCN) and Generative Adversarial Network (GAN) are the deep learning method proposed for this research. The purpose of this research is to analyze how well the TCN-GAN model predicts stock prices. Previous researches show both TCN and GAN perform well on time series data. TCN excels in analyzing time-series data while GAN enhances training by generating realistic simulations. By combining the strength of both models, this approach aims to enhance stock price prediction accuracy. The proposed model uses TCN as the generator within the GAN framework and a Multilayer Perceptron (MLP) as the discriminator. TCN handles the prediction task and is trained using the GAN model. The model is trained over 500 epochs, with a learning rate of 0.0004 for the generator and 0.0001 for the discriminator. During each epoch, the generator is updated twice to enhance its performance. The resulting model achieves a MAPE score of 2.16% and an RMSE score of 814.25 on the testing dataset, demonstrating excellent performance in stock price prediction despite significant price variations.
Downloads
References
Aggarwal, A., Mittal, M., & Battineni, G. (2021). Generative adversarial network: An overview of theory and applications. In International Journal of Information Management Data Insights (Vol. 1, Issue 1). Elsevier Ltd. https://doi.org/10.1016/j.jjimei.2020.100004
Altinbilek, H. F., Nar, H., Aksu, S., & Kizil, Ü. (2022). Sensory Precipitation Forecast Using Artificial Neural Networks and Decision Trees. Journal of Advanced Research in Natural and Applied Sciences, 8(2), 309–321. https://doi.org/10.28979/jarnas.984312
Bai, S., Zico Kolter, J., & Koltun, V. (2022). An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. http://github.com/locuslab/TCN.
Bi, J., Zhang, X., Yuan, H., Zhang, J., & Zhou, M. C. (2022). A Hybrid Prediction Method for Realistic Network Traffic With Temporal Convolutional Network and LSTM. IEEE Transactions on Automation Science and Engineering, 19(3), 1869–1879. https://doi.org/10.1109/TASE.2021.3077537
Budiprasetyo, G., Hani’ah, M., & Aflah, D. Z. (2023). Prediksi Harga Saham Syariah Menggunakan Algoritma Long Short-Term Memory (LSTM). Jurnal Nasional Teknologi Dan Sistem Informasi, 8(3), 164–172. https://doi.org/10.25077/teknosi.v8i3.2022.164-172
Chan Hong, G., Nur Syazreen, A., & Patrick, G. (2023). TCN for transient simulation of highspeed channels. 2023 THE AUTHORS, 1110–0168. https://doi.org/10.1016/j.aej.2023.05.059
Chen, Y., Kang, Y., Chen, Y., & Wang, Z. (2019). Probabilistic Forecasting with Temporal Convolutional Neural Network. http://arxiv.org/abs/1906.04397
Deng, S., Chen, J., Zhang, N., Pan, J. Z., Zhang, W., & Chen, H. (2019). Knowledge-driven stock trend prediction and explanation via temporal convolutional network. The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019, 678–685. https://doi.org/10.1145/3308560.3317701
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139–144. https://doi.org/10.1145/3422622
Hodson, T. O. (2022). Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not. In Geoscientific Model Development (Vol. 15, Issue 14, pp. 5481–5487). Copernicus GmbH. https://doi.org/10.5194/gmd-15-5481-2022
Jabbar, A., Li, X., & Omar, B. (2021). A Survey on Generative Adversarial Networks: Variants, Applications, and Training.
Kang, Z., Guoqiang, Z., Junyu, D., Shenke, W., & Yong, W. (2019). Stock Market Prediction Based on Generative Adversarial Network. 2018 International Conference on Identification, Information and Knowledge in the Internet of Things, 400–406. https://doi.org/10.1016/j.procs.2019.01.256
Lan, L., You, L., Zhang, Z., Fan, Z., Zhao, W., Zeng, N., Chen, Y., & Zhou, X. (2020). Generative Adversarial Networks and Its Applications in Biomedical Informatics. In Frontiers in Public Health (Vol. 8). Frontiers Media S.A. https://doi.org/10.3389/fpubh.2020.00164
Lara-Benítez, P., Carranza-García, M., Luna-Romera, J. M., & Riquelme, J. C. (2020). Temporal convolutional networks applied to energy-related time series forecasting. Applied Sciences (Switzerland), 10(7). https://doi.org/10.3390/app10072322
Ngo, D. (2021). Predicting Stocks with LSTM-based DRNN and GAN [San Jose State University]. https://doi.org/10.31979/etd.fjsz-y926
Nurwita, N. (2023). Pengaruh Likuiditas dan Profitabilitas Terhadap Harga Saham Pada PT Indo Tambang Raya Megah Tbk. JEMSI (Jurnal Ekonomi, Manajemen, Dan Akuntansi), 9(2), 492–500. https://doi.org/10.35870/jemsi.v9i2.1073
Santoso, A., & Ariyanto, G. (2019). Implementasi Deep Learning Berbasis KERAS untuk Pengenalan Wajah. Jurnal Teknik Elektro, 18(01). https://www.mathworks.com/discovery/convol
Sulaiman, D. C., & Matius, T. M. S. (2023). Web-based Writing Learning Application of Basic Hanacaraka Using Convolutional Neural Network Method. Ultimatics : Jurnal Teknik Informatika, 15(1).
Sunitha, G., Arunachalam, R., Abd-Elnaby, M., Eid, M. M. A., & Rashed, A. N. Z. (2022). A comparative analysis of deep neural network architectures for the dynamic diagnosis of COVID-19 based on acoustic cough features. International Journal of Imaging Systems and Technology, 32(5), 1433–1446. https://doi.org/10.1002/ima.22749
Tambunan, D. (2020). Investasi Saham di Masa Pandemi COVID-19. Jurnal Sekretari Dan Manajemen, 4(2). http://ejournal.bsi.ac.id/ejurnal/index.php/widyacipta
Yujie, L., Hongbin, D., Xingmei, W., & Shuang, H. (2019). Time Series Prediction Based on Temporal Convolutional Network. 2019 IEEE ICIS 2019, 978-1-7281-0801-8/19/$31.00.
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2025 Lim Yong Teck, Angelina Pramana Thenata

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.