SHS Web Conf.
Volume 181, 20242023 International Conference on Digital Economy and Business Administration (ICDEBA 2023)
|Number of page(s)
|Financial Analysis and Stock Market Strategies
|17 January 2024
Research on gold price forecasting based on lstm and linear regression
Ocean University of China, Faculty of Information Science and Engineering, Qingdao, 266100, China
* corresponding author: firstname.lastname@example.org
Gold price forecasting is critical in financial decision-making, providing valuable insights for in-vestors and stakeholders in the gold market. Deep learning methods have witnessed remarkable progress in various domains, including image recognition and sentiment analysis. This paper integrates LSTM (Long Short-Term Memory) and Linear Regression models to forecast the rise and fall of gold prices. The analysis of the prediction accuracy regarding the rise and fall of the daily gold price reveals that the LSTM model achieved an accuracy rate of 50.67%, while the Linear Regression model achieved a slightly higher accuracy rate of 53.02%. By combining the strengths of these models, this research provides valuable insights to investors in the gold markets.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.