Gold Price Forecasting and Market Analysis Using LSTM and Visibility Graph
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Much research has focused on financial market time series. This thesis presents a comprehensive analysis of gold price prediction and market structure using a visibility graph network. With a specific focus on the years 2008 and 2024, we discuss the price dynamics of gold in two currencies: Bangladeshi Taka (BDT) and US Dollars (USD), using two complementary methods: Long Short-Term Memory (LSTM) forecasting and Visibility Graph (VG) analysis, which revealed the structural complexity of the market. The LSTM model captures the non-linear movements of highly volatile gold prices, giving it strong predictive capability and providing useful insights for investors and policymakers in Bangladesh. Gold's popularity as a safe-haven investment during economic uncertainty underscores the importance of forecasting, and our results show that LSTM can generate effective long-term projections. At the same time, by constructing gold price time series as complex networks using the VG algorithm, we study structural characteristics every year. Important network measurements-Average Clustering Coefficient, Mean Degree, Modularity, Average Shortest Path Length, Structural Entropy and Global Reaching Centrality are calculated to find underlying structures and market dynamics. Comparative analysis between BDT and USD markets reveals that the Bangladeshi gold market exhibits higher modularity and entropy, indicating a more diversified structure. Both markets show signs of stability over time, with significant structural deviations during major global financial events. Degree distributions follow a power law in most years, indicating fractal characteristics in gold price dynamics. Together, the LSTM forecasting and visibility graph-based structural analysis offer a dual perspective on the gold market— capturing both future price trends and the underlying market complexity. These insights not only deepen our understanding of gold market behaviour but also provide a foundation for informed decision-making in investment and economic policy.
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Madhika, Y.R., Kusrini, K. and Hidayat, T., “Gold Price Prediction Using the ARIMA and LSTM Models”, Sinkron: Jurnal dan Penelitian Teknik Informatika, e-ISSN: 2541-2019, p-ISSN: 2541-044X, 2023, Vol. 7, No. 3, pp.1255-1264. DOI: 10.33395/sinkron.v8i3.12461
Zhu, J. and Wei, D., “Analysis of stock market based on visibility graph and structure entropy”, Physica A: Statistical Mechanics and its Applications, 2021, Vol. 576, p.126036, DOI: 10.1016/j.physa.2021.126036
Yunlong Hu and Fuyuanxiao, “An efficient forecasting method for time series based on visibility graph and multi-subgraph similarity”, Journal of Chaos, Solitons & Fractals, 2022, Vol. 160. DOI: 10.1016/j.chaos.2022.112243
Guo, Y., “Research on the application of gold price prediction based on LSTM model”, Information Systems and Economics, ISSN 2523-6407, 2024, Vol. 5, No. 4, pp. 112-118. DOI: 10.23977/infse.2024.050414
Salim, M. and Djunaidy, A.Zainuddin, M., “Development of a CNN-LSTM Approach with Images as Time-Series Data Representation for Predicting Gold Prices.”, Journal of Procedia, 2024, Vol.234, p. 333-340. DOI: 10.1016/j.procs.2024.03.007
Zainuddin, M., Rachmat, R., “Multi-Time Frame Gold Price Forecasting Using Long Short-Term Memory”, Journal of Krisnadana, 2026, Vol.5, No. 2, pp. 399-409. DOI: 10.58982/krisnadana.v5i2.1110
Rakib, M.I., Nobi, A. and Lee, J.W., “Structure and dynamics of financial networks by feature ranking method”, Scientific Reports, 2021, Vol.11, No. 1, Article 17618. DOI: 10.1038/s41598-021-97100-1
Chen, S., “Predicting Chinese Gold Prices: A Comparative Study of LSTM and Random Forest Models”, Proceedings of the 2025 International Conference on Financial Risk and Investment Management (ICFRIM 2025), 2025, Vol. 333, p. 202-208, Springer Nature, DOI: 10.2991/978-94-6463-748-9_24
Siami-Namini, S., Tavakoli, N. and Namin, A.S., “A comparison of ARIMA and LSTM in forecasting time series”. 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, pp. 1394-1401. DOI: 10.1109/ICMLA.2018.00227
Mokashi, M., Bhoite, S., Nayakwadi, V., Lomte, V., & Pokale, N., “Comparative Analysis of Long Short-Term Memory Networks and Artificial Neural Networks for Gold Price Prediction”, Journal of Information Systems Engineering and Management, e-ISSN: 2468-4376, 2025, Vol. 10, Iss. 2, pp. 933-980. [Online]. Available: file:///C:/Users/USER/Downloads/A_Comparative_Analysis_of_Long_Short-Term_Memory_N-2.pdf
Amira, B., “Gold Price Forecasting Using Stacked LSTM Neural Networks: A Deep Learning Approach”, El-Manhel Economy, 2026, Vol. 9, No. 1, pp. 775-788. [Online]. Available:
file:///C:/Users/USER/Downloads/gold-price-forecasting-using-stacked-lstm-neural-networks_-a-deep-learning-approach-1.pdf
Boongasame, L., Viriyaphol, P., Tassanavipas, K. and Temdee, P., “Gold-price forecasting method using long short-term memory and the Association Rule”, Journal of Mobile Multimedia, 2022, Vol. 19, Iss.1, pp.165-186. DOI: 10.13052/jmm1550-4646.1919
Zhu, Jia and Wei, Daijun, “Analysis of stock market based on visibility graph and structure entropy”. Journal of Physics A: Statistical Mechanics and its Applications, 2023, Vol.576, 126036. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0378437121003083?utm_source=chatgpt.com
Bhaduri, S., Ghosh, D. and Ghosh, S., “Fluctuation of USA Gold Price-Revisited with Chaos-based Complex Network Method”, 2016, arXiv:1608.01103 v1. [Online]. Available: https://arxiv.org/abs/1608.01103
Yan, X., Zhang, S., Hu, J., Weng, W. and Wang, L., “Key Points-in-time identification of gold futures market: a complex network approach”. Fluctuation and Noise Letters, 2023, Vol.22, Iss.4, Article. 2340004. DOI: 10.1142/S0219477523400047
Liu, R. and Chen, Y., “Analysis of stock price motion asymmetry via visibility-graph algorithm”, Frontiers in Physics, 2020, Vol. 8, Article. 539521, pp. 1-13. DOI: 10.3389/fphy.2020.539521
Merji, S., Aloui, C. and Khan, N., “The Gold Stock Nexus: Assessing the causality dynamics based on advanced multi-scale approaches”, Journal of Resource Policy, 2024, Vol. 88, Article. 104395. DOI: 10.1016/j.resourpol.2023.104395