A multi-horizon CNN-GRU architecture for gold price forecasting
TL;DR
These findings confirm that fusing convolutional pattern mining with streamlined recurrent gating mechanisms significantly enhances predictive precision while optimizing computational efficiency for high-dimensional financial datasets.
Abstract
Precise price forecasting in gold markets constitutes a fundamental objective for financial risk management systems operating within volatile environments. Conventional econometric frameworks and standalone deep learning models frequently exhibit insufficient capacity to capture the non-linear dependencies and chaotic dynamics that characterize these financial sequences. This research establishes a hybrid neural network architecture that integrates one-dimensional convolutional neural network (1D-CNN) layers with gated recurrent units (GRU) to synergize localized feature extraction with long-term temporal sequence modeling. The methodology incorporates a specific sliding window preprocessing algorithm to transform multivariate time-series data into structured three-dimensional tensors, thereby preserving critical cross-feature interactions for the learning process. Empirical validation utilizing price records from 2000 to 2025 confirms that the proposed Hybrid CNN-GRU framework consistently achieves predictive accuracy superior to the standalone 1D-CNN, the Hybrid CNN-Long Short-Term Memory (LSTM), and the Hybrid CNN-Dual GRU benchmarks across all experimental window configurations. Specifically, in the ten-day window configuration, the model attained a maximum coefficient of determination ( ) of 0.9962 ± 0.0008, a mean absolute error (MAE) of 0.0178 ± 0.0018, and a mean squared error (MSE) of 0.0006 ± 0.0001. These findings confirm that fusing convolutional pattern mining with streamlined recurrent gating mechanisms significantly enhances predictive precision while optimizing computational efficiency for high-dimensional financial datasets.