Deep Learning-based Gold Price Prediction: A Novel Approach using Time Series Analysis

Hewa Majeed Zangana, Salah Ramadan Obeyd

Abstract


This paper presents a deep learning-based system for predicting gold prices using historical data. The system leverages Long Short-Term Memory (LSTM), a specialized recurrent neural network architecture, to capture temporal dependencies and patterns in the time series data of gold prices. A comprehensive dataset of historical gold prices is used, and the model is trained on a sequence of past data points to predict future prices. The data is preprocessed using normalization techniques to improve the performance of the model. Experimental results demonstrate the effectiveness of the proposed model in providing accurate price predictions, offering potential utility in financial forecasting and decision-making processes. The system's performance is evaluated through visualization and statistical metrics, illustrating its capacity to track gold price trends and predict future market movements. This work contributes to the growing field of time series forecasting by applying deep learning techniques to financial markets.

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References


S. K. Singh, N. Gupta, S. Baliyan, and P. K. Mishra, “Gold Price Prediction Using Machine Learning Algorithm,” NeuroQuantology, vol. 20, no. 20, p. 2998, 2022.

R. Bala and R. P. Singh, “A dual-stage advanced deep learning algorithm for long-term and long-sequence prediction for multivariate financial time series,” Appl Soft Comput, vol. 126, p. 109317, 2022.

M. Mohtasham Khani, S. Vahidnia, and A. Abbasi, “A deep learning-based method for forecasting gold price with respect to pandemics,” SN Comput Sci, vol. 2, no. 4, p. 335, 2021.

A. H. Baradaran, M. Bohlouli, and M. R. J. Motlagh, “Decoding Tomorrow’s Gold Prices: A Comparative Study of GRU and CNN-LSTM in the Iranian Market,” in 2024 10th International Conference on Web Research (ICWR), IEEE, 2024, pp. 329–334.

A. Kanwal, M. F. Lau, S. P. H. Ng, K. Y. Sim, and S. Chandrasekaran, “BiCuDNNLSTM-1dCNN—A hybrid deep learning-based predictive model for stock price prediction,” Expert Syst Appl, vol. 202, p. 117123, 2022.

W. Gong, “Research on gold price forecasting based on lstm and linear regression,” in SHS Web of Conferences, EDP Sciences, 2024, p. 02005.

M. Salim and A. Djunaidy, “Development of a CNN-LSTM Approach with Images as Time-Series Data Representation for Predicting Gold Prices,” Procedia Comput Sci, vol. 234, pp. 333–340, 2024.

M. Mousapour Mamoudan, A. Ostadi, N. Pourkhodabakhsh, A. M. Fathollahi-Fard, and F. Soleimani, “Hybrid neural network-based metaheuristics for prediction of financial markets: a case study on global gold market,” J Comput Des Eng, vol. 10, no. 3, pp. 1110–1125, 2023.

H. Liu and Z. Long, “An improved deep learning model for predicting stock market price time series,” Digit Signal Process, vol. 102, p. 102741, 2020.

I. E. Livieris, S. Stavroyiannis, E. Pintelas, and P. Pintelas, “A novel validation framework to enhance deep learning models in time-series forecasting,” Neural Comput Appl, vol. 32, no. 23, pp. 17149–17167, 2020.

P. Hajek and J. Novotny, “Fuzzy rule-based prediction of gold prices using news affect,” Expert Syst Appl, vol. 193, p. 116487, 2022.

S. Das, T. P. Sahu, and R. R. Janghel, “Oil and gold price prediction using optimized fuzzy inference system based extreme learning machine,” Resources Policy, vol. 79, p. 103109, 2022.

J. Wang, “A novel metal futures forecasting system based on wavelet packet decomposition and stochastic deep learning model,” Applied Intelligence, vol. 52, no. 8, pp. 9334–9352, 2022.

S. Nallamothu, K. Rajyalakshmi, and P. Arumugam, “Gold Price Prediction Using Skewness and Kurtosis Based Generalized Auto-regressive Conditional Heteroskedasticity Approach with Long Short Term Memory Network,” Journal of The Institution of Engineers (India): Series B, pp. 1–13, 2024.

P. Zhang and B. Ci, “Deep belief network for gold price forecasting,” Resources Policy, vol. 69, p. 101806, 2020.

T. Srivastava, I. Mullick, and J. Bedi, “Association mining based deep learning approach for financial time-series forecasting,” Appl Soft Comput, vol. 155, p. 111469, 2024.

C. Zhang, N. N. A. Sjarif, and R. Ibrahim, “Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022,” Wiley Interdiscip Rev Data Min Knowl Discov, vol. 14, no. 1, p. e1519, 2024.

F. Zhang and N. Wen, “Carbon price forecasting: a novel deep learning approach,” Environmental Science and Pollution Research, vol. 29, no. 36, pp. 54782–54795, 2022.

Y. Zhao and G. Yang, “Deep Learning-based Integrated Framework for stock price movement prediction,” Appl Soft Comput, vol. 133, p. 109921, 2023.




DOI: https://doi.org/10.32520/stmsi.v13i6.4651

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