* Corresponding Author
Hide
Show all
Adaptive Moment Estimation On Deep Belief Network For Rupiah Currency Forecasting
https://doi.org/10.22146/ijccs.39071 Abram Setyo Prabowo(1*), Agus Sihabuddin(2), Azhari SN(3)
(1) Program Studi S2 Ilmu Komputer,FMIPA UGM, Yogyakarta, Indonesia
(2) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta, Indonesia
(3) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta, Indonesia
(*) Corresponding Author
Abstract
One approach that is often used in forecasting is artificial neural networks (ANN), but ANNs have problems in determining the initial weight value between connections, a long time to reach convergent, and minimum local problems.
Deep Belief Network (DBN) model is proposed to improve ANN's ability to forecast exchange rates. DBN is composed of a Restricted Boltzmann Machine (RBM) stack. The DBN structure is optimally determined through experiments. The Adam method is applied to accelerate learning in DBN because it is able to achieve good results quickly compared to other stochastic optimization methods such as Stochastic Gradient Descent (SGD) by maintaining the level of learning for each parameter.
Tests are carried out on USD / IDR daily exchange rate data and four evaluation criteria are adopted to evaluate the performance of the proposed method. The DBN-Adam model produces RMSE 59.0635004, MAE 46.406739, MAPE 0.34652. DBN-Adam is also able to reach the point of convergence quickly, where this result is able to outperform the DBN-SGD model.
Keywords
Full Text:
PDFReferences
[1] D. Semaan, A. Harb, and A. Kassem, “Forecasting Exchange Rates: Artificial Neural Networks Vs Regression,” Third Int. Conf. e-Technologies Networks Dev., vol. 2, pp. 156–161, 2014.
[2] G. E. Hinton, S. Osindero, and Y. W. Teh, “A fast learning algorithm for deep belief nets.,” Neural Comput., vol. 18, no. 7, pp. 1527–54, 2006.
[3] T. Hirata, T. Kuremoto, M. Obayashi, S. Mabu, and K. Kobayashi, “Time Series Prediction Using DBN and ARIMA,” Proc. Int Comput. Appl. Technol. Conf, pp. 24–29, 2015.
[4] T. Kuremoto, M. Obayashi, K. Kobayashi, T. Hirata, and S. Mabu, “Forecast chaotic time series data by DBNs,” 2014 7th Int. Congr. Image Signal Process., pp. 1130–1135, 2014.
[5] I. Zulfa and E. Winarko, “Sentimen Analisis Tweet Berbahasa Indonesia Dengan Deep Belief Network,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 11, no. 2, p. 187, 2017.
[6] A. Sihabuddin and S. Hartati, “Exchange rates forecasting using nonlinear autoregressive,” AIP Conf. Proc., vol. 1755, 2016.
[7] A. Sihabuddin, D. Rosadi, and S. Utami, “An Empirical Comparative Forecast Accuracy of Exponential Smoothing and Nonlinear Autoregressive Models on Six Major Rates,” Int. J. Comput. Sci. Inf. Secur., vol. 15, no. 1, pp. 670–673, 2017.
[8] F. Shen, J. Chao, and J. Zhao, “Forecasting exchange rate using deep belief networks and conjugate gradient method,” Neurocomputing, vol. 167, pp. 243–253, 2015.
[9] T. Hirata, T. Kuremoto, M. Obayashi, S. Mabu, and K. Kobayashi, “Forecasting Real Time Series Data using Deep Belief Net and Reinforcement Learning,” Int. Conf. Artif. Life Robot., no. Icarob, pp. 658–661, 2017.
[10] T. Kuremoto, S. Kimura, K. Kobayashi, and M. Obayashi, “Time series forecasting using a deep belief network with restricted Boltzmann machines,” Neurocomputing, vol. 137, no. Jsps 23500181, pp. 47–56, 2014.
[11] S. Ruder, “An overview of gradient descent optimization algorithms,” CoRR, vol. abs/1609.04747, 2016.
[12] D. P. Kingma and J. L. Ba, “Adam: A Method for Stochastic Optimization,” Int. Conf. Learn. Represent., pp. 1–13, Dec. 2015.
[13] K. Maris, K. Nikolopoulos, K. Giannelos, and V. Assimakopoulos, “Options trading driven by volatility directional accuracy,” Appl. Econ., 2007.
[14] A. M. Oyewale, “Evaluation of Artificial Neural Networks in Foreign Exchange Forecasting,” Am. J. Theor. Appl. Stat., vol. 2, no. 4, pp. 94–101, 2013.
DOI: https://doi.org/10.22146/ijccs.39071
Article Metrics
Abstract views : 3787
| 
views : 3163
Refbacks
- There are currently no refbacks.
Copyright (c) 2019 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1