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<p>Gold prices hold a significant place in the global economy as they reflect the economic health, which influences markets, investments, and currency values. Industries that rely on commodities, investors and decision-makers, need accurate forecasting gold prices. Existing models for gold price forecasting, struggle with overfitting, poor adaptability and difficulty in handling volatile or long term trend changes. For this research, various deep learning models were evaluated, which includes Long Short-Term Memory, Convolutional Neural Networks, hybrid LSTM-CNN. The proposed hybrid model of LSTM-Autoencoders, to predict the gold prices for the data taken from September 2000 to January 2024. We also examine the impact of external parameters such as US dollar price, silver price, and crude oil price on forecasting gold prices. Furthermore, a comparative analysis of these external parameters shows that, only gold prices as a prediction parameter yields the highest accuracy across various evaluation metrics. While the silver prices showed some association with the gold price prediction, crude oil had a comparatively low predictive value. Additionally the LSTM-Autoencoders hybrid model has shown the highest accuracy outperforming the other models, while addressing the challenge of overfitting effectively. The results and findings from this study, aid in exploring the role of deep learning in financial time series domain, offering insights which contributes to financial analysts market strategist and economic forecasters.</p>

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Forecasting gold price using hybrid deep neural network LSTM-autoencoder

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月22日

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  • Agampreet Saini,
  • Rahul Kumar Singh,
  • Puneet Sinha

摘要

Gold prices hold a significant place in the global economy as they reflect the economic health, which influences markets, investments, and currency values. Industries that rely on commodities, investors and decision-makers, need accurate forecasting gold prices. Existing models for gold price forecasting, struggle with overfitting, poor adaptability and difficulty in handling volatile or long term trend changes. For this research, various deep learning models were evaluated, which includes Long Short-Term Memory, Convolutional Neural Networks, hybrid LSTM-CNN. The proposed hybrid model of LSTM-Autoencoders, to predict the gold prices for the data taken from September 2000 to January 2024. We also examine the impact of external parameters such as US dollar price, silver price, and crude oil price on forecasting gold prices. Furthermore, a comparative analysis of these external parameters shows that, only gold prices as a prediction parameter yields the highest accuracy across various evaluation metrics. While the silver prices showed some association with the gold price prediction, crude oil had a comparatively low predictive value. Additionally the LSTM-Autoencoders hybrid model has shown the highest accuracy outperforming the other models, while addressing the challenge of overfitting effectively. The results and findings from this study, aid in exploring the role of deep learning in financial time series domain, offering insights which contributes to financial analysts market strategist and economic forecasters.