Prediksi Indeks BEI dengan Ensemble Convolutional Neural Network dan Long Short-Term Memory

  • Harya Widiputra Perbanas Institute
  • Adele Mailangkay Perbanas Institute
  • Elliana Gautama Perbanas Institute
Keywords: BEI indexes, ensemble, Convolutional Neural Network, Long Short-Term Memory, deep learning, time series data


The Indonesian Stock Exchange (IDX) stock market index is one of the main indicators commonly used as a reference for national economic conditions. The value of the stock market index is often being used by investment companies and individual investors to help making investment decisions. Therefore, the ability to predict the stock market index value is a critical need. In the fields of statistics and probability theory as well as machine learning, various methods have been developed to predict the value of the stock market index with a good accuracy. However, previous research results have found that no one method is superior to other methods. This study proposes an ensemble model based on deep learning architecture, namely Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), called the CNN-LSTM. To be able to predict financial time series data, CNN-LSTM takes feature from CNN for extraction of important features from time series data, which are then integrated with LSTM feature that is reliable in processing time series data. Results of experiments on the proposed CNN-LSTM model confirm that the hybrid model effectively provides better predictive accuracy than the stand-alone time series data forecasting models, such as CNN and LSTM.



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R. Vanaga and B. Sloka, “Financial and capital market commission financing: aspects and challenges,” Journal of Logistics, Informatics and Service Science, vol. 7, no. 1, pp. 17–30, 2020.

L. Zhang and H. Kim, “The influence of financial service characteristics on use intention through customer satisfaction with mobile fintech,” Journal of System and Management Sciences, vol. 10, no. 2, pp. 82–94, 2020.

L. Badea, V. Ionescu, and A.-A. Guzun, “What is the causal relationship between stoxx europe 600 sectors? But between large firms and small firms?” Economic Computation And Economic Cybernetics Studies And Research, vol. 53, no. 3, pp. 5–20, 2019.

J. Sousa, J. Montevechi, and R. Miranda, “Economic lot-size using machine learning, parallelism, metaheuristic and simulation,” Journal of Logistics, Informatics and Service Science, vol. 18, no. 2, pp. 205–216, 2019.

A. Coser, M. M. Maer-Matei, and C. Albu, “Predictive models for loan default risk assessment,” Economic Computation And Economic Cybernetics Studies And Research, vol. 53, no. 2, pp. 149–165, 2019.

R. Qiao, “Stock prediction model based on neural network,” Operations Research and Management Science, vol. 28, no. 10, pp. 132–140, 2019.

C. Jung and R. Boyd, “Forecasting UK stock prices,” Applied Financial Economics, vol. 6, no. 3, pp. 279–286, 1996.

W. Bleesser and P. Liicoff, “Predicting stock returns with bayesian vector autoregressive,” Data Analysis, Machine Learning and Applications, vol. 1, pp. 499–506, 2005.

A. Adebiyi, A. Adewumi, and C. Ayo, “Stock price prediction using the ARIMA model,” in Proceedings of the 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, IEEE, Cambridge, UK, March 2014.

C. Zhang, X. Cheng, and M. Wang, “An empirical research in the stock market of Shanghai by GARCH model,” Operations Research and Management Science, vol. 4, pp. 144–146, 2005.

Q. Yang and C. Wang, “A study on forecast of global stock indices based on deep LSTM neural network,” Statistical Research, vol. 36, no. 6, pp. 65–77, 2019.

K.-S. Moon and H. Kim, “Performance of deep learning in prediction of stock market volatility,” Economic Computation And Economic Cybernetics Studies And Research, vol. 53, no. 2, pp. 77–92, 2019.

B. Kuechler, V. Vaishnavi, "Design Science Research in Information Systems," 2004.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.

B. S. Kim and T. G. Kim, “Cooperation of simulation and data model for performance analysis of complex systems,” International Journal of Simulation Modelling, vol. 18, no. 4, pp. 608–619, 2019.

Y. Sun, Y. Liang, and W. Zhang, “Optimal partition algorithm of the RBF neural network and its application to financial time series forecasting,” Neural Computing and Applications, vol. 14, pp. 1441–1449, 2005.

D. Ciresan, U. Meier, and J. Schmidhuber, "Multi-column deep neural networks for image classification," 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3642–3649, 2012.

M.L. Brocardo, I. Traore, I. Woungang, and M.S. Obaidat, "Authorship verification using deep belief network systems," Int J Commun Syst., pp. 1013-1021, 2017.

D. Silver, A. Huang, C. Maddison, A. Guez, L. Sifre, G. Driessche, J. Schrittwieser, I. Antonoglou, and V. Panneershelvam, "Mastering the game of Go with deep neural networks and tree search," Nature, vol. 529 , no. 7587, pp. 484–489, 2016.

A. Marblestone, G. Wayne, and K. Kording, "Toward an Integration of Deep Learning and Neuroscience," Frontiers in Computational Neuroscience, vol. 21, no. 3, pp. 213-230, 2016.

Y. Bengio, A. Courville, and P. Vincent, "Representation Learning: A Review and New Perspectives," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798–1828, 2013.

L. Deng, and D. Yu, "Deep Learning: Methods and Applications," Foundations and Trends in Signal Processing, vol. 7, no. 3–4, pp. 1–199, 2014.

Y. Bengio, "Learning Deep Architectures for AI," Foundations and Trends in Machine Learning, vol. 2, no. 1, pp. 1–127, 2009.

Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, no. 7553, pp. 436–444, 2015.

J. Schmidhuber, "Deep Learning in Neural Networks: An Overview," Neural Networks, vol. 61, pp, 85–117, 2015.

A. Yadav, C. K. Jha, and A. Sharan, “Optimizing LSTM for time series prediction in Indian stock market,” Procedia Computer Science, vol. 167, pp. 2091–2100, 2020.

H. Y. Kim and C. H. Won, “Forecasting the volatility of stock price index: a hybrid model integrating LSTM with multiple GARCH-type models,” Expert Systems with Applications, vol. 103, pp. 25–37, 2018.

N. C. Petersen, R. Christoffer, F. Rodrigues, and F. C. Pereira, “Multi-output bus travel time prediction with convolutional LSTM neural network,” Expert Systems with Applications, vol. 120, pp. 426–435, 2019.

W. Lu, J. Li, Y. Li, A. Sun, and J. Wang, "A CNN-LSTM-Based Model to Forecast Stock Prices," Complexity,, 2020.

J. Brownlee, “Deep Learning for Time Series Forecasting Predict the Future with MLPs, CNNs and LSTMs in Python,” Edition v.1.7, Machine Learning Mastery, San Juan, PR 00901, 2020.

How to Cite
Widiputra, H., Adele Mailangkay, & Elliana Gautama. (2021). Prediksi Indeks BEI dengan Ensemble Convolutional Neural Network dan Long Short-Term Memory. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(3), 456 - 465.
Artikel Rekayasa Sistem Informasi