Author(s)

Yuquan Guo ¹

Affiliation(s)

¹ School of Software, Shanxi Agricultural University, Jinzhong, 030801, China

Corresponding Author

Yuquan Guo

ABSTRACT

A gold price prediction model with LSTM (Long Short Term Memory Network) is proposed. The data from 2013 to 2023 are evaluated. The results show that the model has an excellent prediction effect with an accuracy of 96.9%. This research provides new effective methods and ideas in the field of gold price prediction, provides highly valuable and important reference bases for the majority of gold industry practitioners in investment decision-making, risk control, etc., and is of great significance to the stable and efficient development of the gold trading market. This paper uses an LSTM network to predict the future price of gold, and verifies the performance and accuracy of the model through a series of data processing, model training and evaluation steps. The application research of gold price prediction based on LSTM model can provide a reliable price reference for the gold trading market, promote the stable operation of the market, reduce transaction costs, and improve the trading efficiency of the market.

KEYWORDS

Gold Price Prediction, LSTM Model, Forgetting Mechanisms, Time Series

CITE THIS PAPER

Yuquan Guo, Research on the application of gold price prediction based on LSTM model. Information Systems and Economics (2024) Vol. 5: 112-118. DOI: http://dx.doi.org/10.23977/infse.2024.050414.

REFERENCES

[1] Kang Yizhi. Analysis of historical fluctuation trajectory characteristics and influencing factors of gold price [J]. Modern Enterprise, 2022, (09): 94-96.
[2] Shi Yansong, Yang Bo. Prediction of gold futures price by CNN-LSTM model under RNN online learning framework [J]. Modern Information Science & Technology, 2024, 8(11): 141-144+152.
[3] Jia Degao, Liu Chunyu, Liu Jiapeng. Research on gold futures price trend based on hidden Markov model [J]. China Business Review, 2024, (11): 100-103.
[4] Su M , Nie Y , Li J ,et al.Futures markets and the baltic dry index: A prediction study based on deep learning[J]. Research in International Business and Finance, 2024, 71.DOI:10.1016/j.ribaf.2024.102447.
[5] Liang Longyue, Huang Ying. Research on short-term prediction method of gold futures price—COMEX gold futures price data analysis based on CEEMDAN and LSTM model [J]. Price Theory & Practice, 2023, (09): 164-168.
[6] He Linyun. Gold futures price prediction based on ICEEMDAN-SE-SSA-ELM algorithm [J]. Journal of Lanzhou University of Arts and Sciences (Natural Science Edition), 2023, 37(01): 35-39.
[7] Fang Qian, Yang Nan. Construction of nonlinear error correction learning model based on LSTM network [J]. Statistics and Decision, 2022, 38(24): 5-10. 
[8] Man Ke, Wu Liwen, Liu Xiaoli, et al. TBM tunneling parameters and rockburst grade prediction based on CNN-LSTM model [J/OL]. Coal Science and Technology, 1-19 [2024-07-19].
[9] Li Fen, Sun Ling, Wang Yawei, et al. Short-term interval prediction of photovoltaic power based on CEEMDAN-GSA-LSTM and SVR [J]. Journal of Shanghai Jiao Tong University, 2024, 58(06): 806-818.
[10] Kou Yuxuan, Li Sihan, Liu Dunhu, et al. Research on Container Throughput Prediction Model Based on DKELM Selective Deep Integration [J/OL]. CMS, 1-25 [2024-07-20].

Sponsors, Associates, and Links

---

• Accounting, Auditing and Finance
  
  
• Industrial Engineering and Innovation Management
  
  
• Tourism Management and Technology Economy
  
  
• Journal of Computational and Financial Econometrics
  
  
• Financial Engineering and Risk Management
  
  
• Accounting and Corporate Management
  
  
• Social Security and Administration Management
  
  
• Population, Resources & Environmental Economics
  
  
• Statistics & Quantitative Economics
  
  
• Agricultural & Forestry Economics and Management
  
  
• Social Medicine and Health Management
  
  
• Land Resource Management
  
  
• Information, Library and Archival Science
  
  
• Journal of Human Resource Development
  
  
• Manufacturing and Service Operations Management
  
  
• Operational Research and Cybernetics