Quick jump to page content
  • Main Navigation
  • Main Content
  • Sidebar

  • Home
  • Current
  • Archives
  • Join As Reviewer
  • Info
  • Announcements
  • Statistics
  • About
    • About the Journal
    • Submissions
    • Editorial Team
    • Privacy Statement
    • Contact
  • Register
  • Login
  • Home
  • Current
  • Archives
  • Join As Reviewer
  • Info
  • Announcements
  • Statistics
  • About
    • About the Journal
    • Submissions
    • Editorial Team
    • Privacy Statement
    • Contact
  1. Home
  2. Archives
  3. Vo. 6, No. 3, August 2021
  4. Articles

Issue

Vo. 6, No. 3, August 2021

Issue Published : Aug 31, 2021
Creative Commons License

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

Gold price prediction using Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM)

https://doi.org/10.22219/kinetik.v6i3.1253
I Wayan Krisna Gita Santika
School of Computing. Telkom University
Siti Sa'adah
School of Computing. Telkom University
Prasti Eko Yunanto
School of Computing. Telkom University

Corresponding Author(s) : I Wayan Krisna Gita Santika

krisnagita@student.telkomuniversity.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vo. 6, No. 3, August 2021
Article Published : Aug 31, 2021

Share
WA Share on Facebook Share on Twitter Pinterest Email Telegram
  • Abstract
  • Cite
  • References
  • Authors Details

Abstract

Gold has an important role in worldwide economics. Gold is not only used in jewelry but also can be a good deal for investment however several factors can affect the fluctuation in gold which can make the risk of investing in gold is bigger for many people. Therefore, is very important to predict the gold price for people who invest in gold in order to help reduce the investment risk. This study will implement a hybrid method from Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). CNN can extract useful knowledge and learn the internal representation of time-series data, and LSTM networks will identify short-term and long-term dependencies effectively. This research will use daily time frame data and weekly time frame data. This research also tried some experiments to find the best hyperparameters of batch size and epochs in ratio data 60:40 and 80:20. The best result obtained in the daily time of ratio data 60:40 with RMSE 13.67953 and MAE 9,40998, while in ratio data 80:20 has RMSE 15,53199 and MAE 12,78120. In weekly time has obtained the RMSE 38,01949 and MAE 28,32035 for ratio data 60:40 while in ratio data 80:20 the result was RMSE 32,61283 and MAE 22,74638. Those results shows that CNN-LSTM model can predict the trend of daily time frame gold price.

Keywords

Gold Price CNN-LSTM Deep Learning Price Prediction Hyperparameter
Krisna Gita Santika, I. W., Sa’adah, S., & Yunanto, P. E. (2021). Gold price prediction using Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) . Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 6(3). https://doi.org/10.22219/kinetik.v6i3.1253
  • ACM
  • ACS
  • APA
  • ABNT
  • Chicago
  • Harvard
  • IEEE
  • MLA
  • Turabian
  • Vancouver
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. E. Bouri, A. Jain, P. C. Biswal, and D. Roubaud, “Cointegration and nonlinear causality amongst gold, oil, and the Indian stock market: Evidence from implied volatility indices,” Resour. Policy, vol. 52, no. March, pp. 201–206, 2017. https://doi.org/10.1016/j.resourpol.2017.03.003
  2. P. K. Mishra, J. R. Das, and S. K. Mishra, “Gold Price Volatility and Stock Market Returns in India,” Am. J. Sci. Res. ISSN, vol. 1450, no. 9, pp. 47–55, 2010.
  3. D. C. North and R. P. Thomas, The Rise of the Western World. Cambridge University Press, 1973.
  4. I. E. Livieris, E. Pintelas, and P. Pintelas, “A CNN–LSTM model for gold price time-series forecasting,” Neural Comput. Appl., vol. 32, 2020. https://doi.org/10.1007/s00521-020-04867-x
  5. N. A. Zainal and Z. Mustaffa, “A literature review on gold price predictive techniques,” 2015 4th Int. Conf. Softw. Eng. Comput. Syst. ICSECS 2015 Virtuous Softw. Solut. Big Data, pp. 39–44, 2015. https://doi.org/10.1109/ICSECS.2015.7333120
  6. X. Yang, “The Prediction of Gold Price Using ARIMA Model,” Adv. Soc. Sci. Educ. Humanit. Res., vol. 196, no. Ssphe 2018, pp. 273–276, 2019. https://dx.doi.org/10.2991/ssphe-18.2019.66
  7. Z. Xie, X. Lin, Y. Zhong, and Q. Chen, “Research on gold etf forecasting based on lstm,” Proc. - 2019 IEEE Intl Conf Parallel Distrib. Process. with Appl. Big Data Cloud Comput. Sustain. Comput. Commun. Soc. Comput. Networking, ISPA/BDCloud/SustainCom/SocialCom 2019, pp. 1346–1351, 2019. https://doi.org/10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00193
  8. G. DeepikaM, G. Nambiar, M. Rajkumar, and A. Vishwavidhyapeetham, “Forecasting Price and Analysing Factors Influencing The Price of Gold Using ARIMA Model and Multiple Regression Analysis,” vol. 2, no. 11, 2012.
  9. A. D. Dubey, “Gold price prediction using support vector regression and ANFIS models,” 2016 Int. Conf. Comput. Commun. Informatics, ICCCI 2016, 2016. https://doi.org/10.1109/ICCCI.2016.7479929
  10. W. Zou and Y. Xia, “Back propagation bidirectional extreme learning machine for traffic flow time series prediction,” Neural Comput. Appl., vol. 31, no. 11, pp. 7401–7414, 2019. https://doi.org/10.1007/s00521-018-3578-y
  11. J. Zheng, X. Fu, and G. Zhang, “Research on exchange rate forecasting based on deep belief network,” Neural Comput. Appl., vol. 31, pp. 573–582, 2019. https://doi.org/10.1007/s00521-017-3039-z
  12. H. H. Zahrah, S. Sa’adah, and R. Rismala, “Foreign Exchange Rate Prediction Using Long-Short Term Memory: A Case Study in COVID-19 Pandemic,” vol. 6, no. 2, pp. 94–105, 2021.
  13. J. Wong, T. Manderson, M. Abrahamowicz, D. L. Buckeridge, and R. Tamblyn, “Can Hyperparameter Tuning Improve the Performance of a Super Learner?: A Case Study,” Epidemiology, vol. 30, no. 4, pp. 521–531, 2019. https://doi.org/10.1097/EDE.0000000000001027
  14. N. Lavesson and P. Davidsson, “Quantifying the impact of learning algorithm parameter tuning,” Proc. Natl. Conf. Artif. Intell., vol. 1, no. 1, pp. 395–400, 2006.
  15. K. Chen, Y. Zhou, and F. Dai, “A LSTM-based method for stock returns prediction: A case study of China stock market,” Proc. - 2015 IEEE Int. Conf. Big Data, IEEE Big Data 2015, pp. 2823–2824, 2015. https://doi.org/10.1109/BigData.2015.7364089
  16. D. A. Nasution, H. H. Khotimah, and N. Chamidah, “Perbandingan Normalisasi Data untuk Klasifikasi Wine Menggunakan Algoritma K-NN,” Comput. Eng. Sci. Syst. J., vol. 4, no. 1, p. 78, 2019. https://doi.org/10.24114/cess.v4i1.11458
  17. Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015. https://doi.org/10.1038/nature14539
  18. K. O’Shea and R. Nash, “An Introduction to Convolutional Neural Networks,” pp. 1–11, 2015.
  19. Suyanto, Machine Learning Tingkat Dasar dan Lanjut. Bandung: Informatika, 2018.
  20. Suyanto, Modernisasi Machine Learning untuk Big Data. Bandung: Informatika, 2019.
  21. Z. He, J. Zhou, H. N. Dai, and H. Wang, “Gold price forecast based on LSTM-CNN model,” Proc. - IEEE 17th Int. Conf. Dependable, Auton. Secur. Comput. IEEE 17th Int. Conf. Pervasive Intell. Comput. IEEE 5th Int. Conf. Cloud Big Data Comput. 4th Cyber Sci., pp. 1046–1053, 2019. https://doi.org/10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00188
  22. L. Seymour, P. J. Brockwell, and R. A. Davis, Introduction to Time Series and Forecasting., vol. 92, no. 440. 1997.
  23. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, Nov. 1997. https://doi.org/10.1162/neco.1997.9.8.1735
  24. R. Dey and F. M. Salemt, “Gate-variants of Gated Recurrent Unit (GRU) neural networks,” Midwest Symp. Circuits Syst., vol. 2017-Augus, no. 2, pp. 1597–1600, 2017. https://doi.org/10.1109/MWSCAS.2017.8053243
  25. Z. Chang, Y. Zhang, and W. Chen, “Effective Adam-Optimized LSTM Neural Network for Electricity Price Forecasting,” Proc. IEEE Int. Conf. Softw. Eng. Serv. Sci. ICSESS, vol. 2018-Novem, no. Figure 1, pp. 245–248, 2019. https://doi.org/10.1109/ICSESS.2018.8663710
  26. D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–15, 2015.
  27. T. Chai and R. R. Draxler, “Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature,” Geosci. Model Dev., vol. 7, no. 3, pp. 1247–1250, 2014. https://doi.org/10.5194/gmd-7-1247-2014, 2014
Read More

References


E. Bouri, A. Jain, P. C. Biswal, and D. Roubaud, “Cointegration and nonlinear causality amongst gold, oil, and the Indian stock market: Evidence from implied volatility indices,” Resour. Policy, vol. 52, no. March, pp. 201–206, 2017. https://doi.org/10.1016/j.resourpol.2017.03.003

P. K. Mishra, J. R. Das, and S. K. Mishra, “Gold Price Volatility and Stock Market Returns in India,” Am. J. Sci. Res. ISSN, vol. 1450, no. 9, pp. 47–55, 2010.

D. C. North and R. P. Thomas, The Rise of the Western World. Cambridge University Press, 1973.

I. E. Livieris, E. Pintelas, and P. Pintelas, “A CNN–LSTM model for gold price time-series forecasting,” Neural Comput. Appl., vol. 32, 2020. https://doi.org/10.1007/s00521-020-04867-x

N. A. Zainal and Z. Mustaffa, “A literature review on gold price predictive techniques,” 2015 4th Int. Conf. Softw. Eng. Comput. Syst. ICSECS 2015 Virtuous Softw. Solut. Big Data, pp. 39–44, 2015. https://doi.org/10.1109/ICSECS.2015.7333120

X. Yang, “The Prediction of Gold Price Using ARIMA Model,” Adv. Soc. Sci. Educ. Humanit. Res., vol. 196, no. Ssphe 2018, pp. 273–276, 2019. https://dx.doi.org/10.2991/ssphe-18.2019.66

Z. Xie, X. Lin, Y. Zhong, and Q. Chen, “Research on gold etf forecasting based on lstm,” Proc. - 2019 IEEE Intl Conf Parallel Distrib. Process. with Appl. Big Data Cloud Comput. Sustain. Comput. Commun. Soc. Comput. Networking, ISPA/BDCloud/SustainCom/SocialCom 2019, pp. 1346–1351, 2019. https://doi.org/10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00193

G. DeepikaM, G. Nambiar, M. Rajkumar, and A. Vishwavidhyapeetham, “Forecasting Price and Analysing Factors Influencing The Price of Gold Using ARIMA Model and Multiple Regression Analysis,” vol. 2, no. 11, 2012.

A. D. Dubey, “Gold price prediction using support vector regression and ANFIS models,” 2016 Int. Conf. Comput. Commun. Informatics, ICCCI 2016, 2016. https://doi.org/10.1109/ICCCI.2016.7479929

W. Zou and Y. Xia, “Back propagation bidirectional extreme learning machine for traffic flow time series prediction,” Neural Comput. Appl., vol. 31, no. 11, pp. 7401–7414, 2019. https://doi.org/10.1007/s00521-018-3578-y

J. Zheng, X. Fu, and G. Zhang, “Research on exchange rate forecasting based on deep belief network,” Neural Comput. Appl., vol. 31, pp. 573–582, 2019. https://doi.org/10.1007/s00521-017-3039-z

H. H. Zahrah, S. Sa’adah, and R. Rismala, “Foreign Exchange Rate Prediction Using Long-Short Term Memory: A Case Study in COVID-19 Pandemic,” vol. 6, no. 2, pp. 94–105, 2021.

J. Wong, T. Manderson, M. Abrahamowicz, D. L. Buckeridge, and R. Tamblyn, “Can Hyperparameter Tuning Improve the Performance of a Super Learner?: A Case Study,” Epidemiology, vol. 30, no. 4, pp. 521–531, 2019. https://doi.org/10.1097/EDE.0000000000001027

N. Lavesson and P. Davidsson, “Quantifying the impact of learning algorithm parameter tuning,” Proc. Natl. Conf. Artif. Intell., vol. 1, no. 1, pp. 395–400, 2006.

K. Chen, Y. Zhou, and F. Dai, “A LSTM-based method for stock returns prediction: A case study of China stock market,” Proc. - 2015 IEEE Int. Conf. Big Data, IEEE Big Data 2015, pp. 2823–2824, 2015. https://doi.org/10.1109/BigData.2015.7364089

D. A. Nasution, H. H. Khotimah, and N. Chamidah, “Perbandingan Normalisasi Data untuk Klasifikasi Wine Menggunakan Algoritma K-NN,” Comput. Eng. Sci. Syst. J., vol. 4, no. 1, p. 78, 2019. https://doi.org/10.24114/cess.v4i1.11458

Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015. https://doi.org/10.1038/nature14539

K. O’Shea and R. Nash, “An Introduction to Convolutional Neural Networks,” pp. 1–11, 2015.

Suyanto, Machine Learning Tingkat Dasar dan Lanjut. Bandung: Informatika, 2018.

Suyanto, Modernisasi Machine Learning untuk Big Data. Bandung: Informatika, 2019.

Z. He, J. Zhou, H. N. Dai, and H. Wang, “Gold price forecast based on LSTM-CNN model,” Proc. - IEEE 17th Int. Conf. Dependable, Auton. Secur. Comput. IEEE 17th Int. Conf. Pervasive Intell. Comput. IEEE 5th Int. Conf. Cloud Big Data Comput. 4th Cyber Sci., pp. 1046–1053, 2019. https://doi.org/10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00188

L. Seymour, P. J. Brockwell, and R. A. Davis, Introduction to Time Series and Forecasting., vol. 92, no. 440. 1997.

S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, Nov. 1997. https://doi.org/10.1162/neco.1997.9.8.1735

R. Dey and F. M. Salemt, “Gate-variants of Gated Recurrent Unit (GRU) neural networks,” Midwest Symp. Circuits Syst., vol. 2017-Augus, no. 2, pp. 1597–1600, 2017. https://doi.org/10.1109/MWSCAS.2017.8053243

Z. Chang, Y. Zhang, and W. Chen, “Effective Adam-Optimized LSTM Neural Network for Electricity Price Forecasting,” Proc. IEEE Int. Conf. Softw. Eng. Serv. Sci. ICSESS, vol. 2018-Novem, no. Figure 1, pp. 245–248, 2019. https://doi.org/10.1109/ICSESS.2018.8663710

D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–15, 2015.

T. Chai and R. R. Draxler, “Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature,” Geosci. Model Dev., vol. 7, no. 3, pp. 1247–1250, 2014. https://doi.org/10.5194/gmd-7-1247-2014, 2014

Author biographies is not available.
Download this PDF file
PDF
Statistic
Read Counter : 450 Download : 303

Downloads

Download data is not yet available.

Quick Link

  • Author Guidelines
  • Download Manuscript Template
  • Peer Review Process
  • Editorial Board
  • Reviewer Acknowledgement
  • Aim and Scope
  • Publication Ethics
  • Licensing Term
  • Copyright Notice
  • Open Access Policy
  • Important Dates
  • Author Fees
  • Indexing and Abstracting
  • Archiving Policy
  • Scopus Citation Analysis
  • Statistic
  • Article Withdrawal

Meet Our Editorial Team

Ir. Amrul Faruq, M.Eng., Ph.D
Editor in Chief
Universitas Muhammadiyah Malang
Google Scholar Scopus
Agus Eko Minarno
Editorial Board
Universitas Muhammadiyah Malang
Google Scholar  Scopus
Hanung Adi Nugroho
Editorial Board
Universitas Gadjah Mada
Google Scholar Scopus
Roman Voliansky
Editorial Board
Dniprovsky State Technical University, Ukraine
Google Scholar Scopus
Read More
 

KINETIK: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
eISSN : 2503-2267
pISSN : 2503-2259


Address

Program Studi Elektro dan Informatika

Fakultas Teknik, Universitas Muhammadiyah Malang

Jl. Raya Tlogomas 246 Malang

Phone 0341-464318 EXT 247

Contact Info

Principal Contact

Amrul Faruq
Phone: +62 812-9398-6539
Email: faruq@umm.ac.id

Support Contact

Fauzi Dwi Setiawan Sumadi
Phone: +62 815-1145-6946
Email: fauzisumadi@umm.ac.id

© 2020 KINETIK, All rights reserved. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License