Gold Price Forecasting and Market Analysis Using LSTM and Visibility Graph
Sujon Kumar1, Md. Rakibul Islam2, Md. Javed Hossain*3
1Sujon Kumar, Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Sonapur, Noakhali, Bangladesh.
2Md. Rakibul Islam, Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Sonapur, Noakhali, Bangladesh.
3Md. Javed Hossain, Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Sonapur, Noakhali, Bangladesh.
Manuscript received on 05 September 2026 | First Revised Manuscript received on 15 September 2026 | Second Revised Manuscript received on 22 September 2026 | Manuscript Accepted on 15 October 2026 | Manuscript published on 30 October 2026 | PP: 9-19 | Volume-16 Issue-1, October 2026 | Retrieval Number: 100.1/ijeat.A480016011026 | DOI: 10.35940/ijeat.A4800.16011026
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Gold Price, Forecast, LSTM, Visibility Graph Network.
Scope of the Article: Computer Science and Engineering