Gold Price Prediction Using a Hybrid Convolutional-Recurrent Neural Network (CNN-GRU)

Document Type : Research Paper

Authors

1 Department of Computer Engineering, Shahid Bahonar University of Kerman, Iran.

2 Department of Economics, Shahid Bahonar University of Kerman, Iran.

Abstract

Gold, as a highly valuable asset, experiences frequent price fluctuations due to economic, political, and supply-demand factors, making accurate forecasting essential for investors and market analysts. A precise prediction model can help identify optimal buying and selling opportunities while minimizing financial risks. This study aims to develop a hybrid predictive model by integrating Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) to enhance the accuracy of gold price forecasting. In this framework, CNN is employed to extract spatial features from historical price data, while GRU captures temporal dependencies, ensuring a more refined prediction. Gold price data from 2004 to 2023 was collected, preprocessed, and normalized before being divided into training and testing datasets. The proposed model was trained using this dataset to identify patterns and trends in gold price movements. Additionally, the implementation of multi-cycle models in the proposed methodology resulted in a 22–48% improvement in prediction accuracy compared to baseline hybrid recurrent models (CNN-LSTM and CNN-BiLSTM) implemented in this study. The experimental results demonstrate that the CNN-GRU model outperforms these alternatives in terms of forecasting precision. Moreover, the proposed hybrid approach exhibits strong generalization capabilities, making it applicable to other financial time series forecasting problems. These findings highlight the effectiveness of combining CNN and GRU in predictive modeling, providing a valuable tool for investors and analysts in making informed financial decisions. The novelty of this study lies in the introduction of a new hybrid CNN-GRU model, applied for the first time specifically for gold price forecasting.

Keywords

Main Subjects

Article Title [Persian]

پیش‌یبنی قیمت طلا به کمک شبکه‌ی عصبی ترکیبی کانولوشنال-بازگشتی

Authors [Persian]

  • فاطمه ایزدی بیدانی 1
  • وحید ستاری نائینی 1
  • زین العابدین صادقی 2
  • امید عابدی 1
1 دانشکده مهندسی کامپیوتر، دانشگاه شهید باهنر، کرمان، ایران.
2 دانشکده اقتصاد، دانشگاه شهید باهنر، کرمان، ایران.
Abstract [Persian]

طلا به‌عنوان یک دارایی بسیار ارزشمند، به دلیل عوامل اقتصادی، سیاسی و عرضه و تقاضا دچار نوسانات قیمتی مکرر می‌شود و این امر پیش‌بینی دقیق آن را برای سرمایه‌گذاران و تحلیلگران بازار ضروری می‌سازد. یک مدل پیش‌بینی دقیق می‌تواند به شناسایی بهترین فرصت‌های خرید و فروش کمک کرده و ریسک‌های مالی را به حداقل برساند. هدف این پژوهش، توسعه یک مدل ترکیبی پیش‌بینی با ادغام شبکه‌های عصبی کانولوشنی (CNN) و واحدهای بازگشتی دروازه‌ای (GRU) به‌منظور افزایش دقت پیش‌بینی قیمت طلا است. در این چارچوب، CNN برای استخراج ویژگی‌های مکانی از داده‌های تاریخی قیمت به‌کار گرفته شده و GRU وابستگی‌های زمانی را ثبت می‌کند تا پیش‌بینی دقیق‌تری حاصل شود. داده‌های قیمت طلا از سال 2004 تا 2023 جمع‌آوری، پیش‌پردازش و نرمال‌سازی شده و سپس به مجموعه‌های آموزش و آزمون تقسیم گردید. مدل پیشنهادی با استفاده از این داده‌ها آموزش داده شد تا الگوها و روندهای تغییر قیمت شناسایی گردد. علاوه بر این، به‌کارگیری مدل‌های چند چرخه در روش پیشنهادی موجب بهبود 22 تا 48 درصدی دقت پیش‌بینی در مقایسه با مدل‌های ترکیبی بازگشتی پایه (CNN-LSTM و CNN-BiLSTM) شد. نتایج آزمایش‌ها نشان داد مدل CNN-GRU از نظر دقت پیش‌بینی عملکرد بهتری نسبت به رقبا دارد. افزون بر این، رویکرد ترکیبی پیشنهادی از قابلیت تعمیم بالایی برخوردار بوده و می‌تواند در سایر مسائل پیش‌بینی سری‌های زمانی مالی نیز به‌کار گرفته شود. نوآوری اصلی این پژوهش، معرفی مدل ترکیبی جدید CNN-GRU است که برای نخستین بار به‌طور ویژه در پیش‌بینی قیمت طلا مورد استفاده قرار گرفته است.

Keywords [Persian]

  • شبکه عصبی کانولوشنی (CNN)
  • شبکه‌های واحد بازگشتی دروازه‌دار (GRU)
  • قیمت طلا
  • یادگیری ماشین

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History

  • Receive Date: 02 February 2025
  • Revise Date: 30 August 2025
  • Accept Date: 31 August 2025

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