Analisis Peramalan Harga Saham Menggunakan Temporal Convolutional Network: Studi Kasus PT Lippo General Insurance Tbk

Muklas Rivai, Ongky Setya Nugraha

Abstract


The stock market has an important role in the Indonesian economy, but share price fluctuations are often difficult to predict accurately. The machine learning algorithm for forecasting stock price movement trends uses a Temporal Convolutional Network (TCN). This method uses a more comprehensive dataset and advanced analysis techniques to capture non-linear and dynamic patterns in stock price data. This research aims to predict the share price of PT Lippo General Insurance Tbk using Temporal Convolutional Network (TCN) to provide a more accurate and reliable forecasting model. The research method uses a quantitative approach with daily historical stock data from 2011 to 2023 which is processed through several stages, including data collection, pre-processing, model development, and performance evaluation.  The results of the study show that the stock price forecasting of PT Lippo General Insurance Tbk using the Temporal Convolutional Network (TCN) method produces values that are relatively close to the actual ones with MSE, RMSE, MAE, and MAPE indicators, respectively, being 11,076.8214; 105.2464; 63.5915; and 2.2369\%. This indicates that the TCN model is able to capture complex temporal patterns in the stock price data of PT Lippo General Insurance Tbk. The forecasting results that have been projected for the next 60 days, that the stock price of PT Lippo General Insurance for the next 60 days will tend to decrease from August 31 to November 23.  

Keywords


Temporal Convolutional Network; Stock Price; Forecasting

Full Text:

PDF

References


U. Kulsum and T. Tamimah, “Instrumen-instrumen investasi syariah sebagai alternatif investasi bodong,” BISEI: Jurnal Bisnis dan Ekonomi Islam, vol. 6, no. 2, pp. 116–134, 2021.

M. F. Mahfuzh and R. V. Yuliantari, “Analisis penerapan artificial neural network algoritma propagasi balik untuk meramalkan harga saham pada bursa efek indonesia,” Journal of Applied Electrical Engineering, vol. 6, no. 1, pp. 1–3, 2022.

A. Mauko, B. Muslimin, and P. Sugiartawan, “Sistem pendukung keputusan kelompok pemilihan saham lq45 dengan menggunakan metode ahp, promethee dan borda,” Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI), vol. 1, no. 1, pp. 25–34, 2018.

M. D. Hamonangan, E. Sumirat, and Y. Sunitiyoso, “Analysing risk & return profiles: A comparative study of the indonesian stock market against international benchmarks,” International Journal of Current Science Research and Review, vol. 7, no. 1, pp. 136–146, 2024.

S. Sumani, N. Puspitasari, and C. F. Sari, “Asymmetric information dan dividend decision pada perusahaan asuransi di indonesia,” BISMA: Jurnal Bisnis dan Manajemen, vol. 14, no. 2, pp. 118–124, 2020.

H. Liu, W. Liu, and Y. Li, “Private information dissemination and noise trading: Implications for price efficiency and market liquidity,” Sustainability, vol. 14, no. 18, p. 11624, 2022.

A. Harel and G. Harpaz, “Forecasting stock prices,” International Review of Economics and Finance, vol. 73, pp. 249–256, 2021.

R. Li, T. Han, and X. Song, “Stock price index forecasting using a multiscale modelling strategy based on frequency components analysis and intelligent optimization,” Applied Soft Computing, vol. 124, 2022.

G. Ning and Y. Zhou, “Application of improved differential evolution algoJJPS | Jambura J. Probab. Stat. Volume 6 | Issue 2 | November 2025M. Rivai, O. S. Nugraha2 – Analisis Peramalan Harga...… 55 rithm in solving equations,” International Journal of Computational Intelligence Systems, vol. 14, no. 199, 2021.

B. Jadhav, S. Kakade, S. Kohakade, S. Bodke, and P. P. Pise, “Survey on stock price forecasting,” Indian Scientific Journal of Research in Engineering and Management, vol. 8, no. 3, pp. 1–5, 2024.

K. V. Kumar and R. Anitha, “A detailed survey to forecast the stock prices by applying machine learning predictive models and artificial intelligence techniques,” in 2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS), 2022. doi: 10.1109/IC3SIS54991.2022.9885309 pp. 1–6.

R. Gnanavel and J. M. Gnanasekar, “A conceptual overview on earlier methodologies focused on stock price prediction,” in 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT), 2023. doi: 10.1109/ICSSIT55814.2023.10061063 pp. 1710–1718.

V. B. Vaghela, T. A. Champaneria, and H. D. Rajput, “Stock price forecasting using the machine learning based on the historical stock prices,” International Journal of Membrane Science and Technology, vol. 10, no. 1, pp. 1902– 1910, 2023.

W. Dong and C. Zhao, “Stock price forecasting based on hausdorff fractional grey model with convolution and neural network,” Mathematical Biosciences and Engineering, vol. 18, no. 4, pp. 3323–3347, 2021.

X. Kan, M. Miao, L. Cao, T. Xu, Y. Li, and J. Jiang, “Stock price prediction based on artificial neural network,” in 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), 2020. doi: 10.1109/MLBDBI51377.2020.00040 pp. 182–185.

Y. You, “Forecasting stock price: A deep learning approach with lstm and hyperparameter optimization,” Highlights in Science, Engineering and Technology, vol. 85, pp. 328–338, 2024.

G. Ang and E. P. Lim, “Temporal implicit multimodal networks for investment and risk management,” ACM Transactions on Intelligent Systems and Technology, vol. 15, no. 2, pp. 1–25, 2024.

Y. Pei, C. J. Huang, Y. Shen, and M. Wang, “A novel model for spot price forecast of natural gas based on temporal convolutional network,” Energies, vol. 16, no. 5, p. 2321, 2023.

P. Lara-Benítez, M. Carranza-García, J. M. Luna-Romera, and J. C. Riquelme, “Temporal convolutional networks applied to energy-related time series forecasting,” Applied Sciences, vol. 10, no. 7, p. 2322, 2020.

R. Wan, S. Mei, J. Wang, M. Liu, and F. Yang, “Multivariate temporal convolutional network: A deep neural networks approach for multivariate time series forecasting,” Electronics, vol. 8, no. 8, 2019.

S. Bai, J. Z. Kolter, and V. Koltun, “An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,” arXiv preprint arXiv:1803.01271, 201




DOI: https://doi.org/10.37905/jjps.v6i2.26817

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Jambura Journal of Probability and Statistics

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

Editorial Office of Jambura Journal of Probability and Statistics:
 
Department of Statistics, 3rd Floor Faculty of Mathematics and Natural Sciences, Universitas Negeri Gorontalo
Jl. Prof. Dr. Ing. B.J Habibie, Tilongkabila Kabupaten Bone Bolango, 96119
Telp: +6285398740008 (Call/SMS/WA)
E-mail: [email protected]