AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A multivariate deep learning framework based on a stacked Long Short-Term Memory (LSTM) network for short-term gold price forecasting that significantly outperforms the traditional Recurrent Neural Network (RNN) in terms of stability, accuracy, and robustness.
STGP-Net is introduced, a novel hybrid deep learning model designed to enhance prediction accuracy by integrating one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) that provides better performance and robustness than alternative hybrid models tested.
The extent to which gold price forecasting, at least from a financial forecasting perspective, can be achieved using a hybrid model of CNN and LSTM is provided, as it showed the capability to detect short-term trends and long-term sequential gaps in gold price series.
The integration of Bagging, Stacked LSTM, and MBB improves model robustness and forecasting accuracy, and can support data-driven decision-making in economic policy, although further research is needed to incorporate additional variables and explore more advanced forecasting architectures.
The findings indicate that the BiLSTM architecture has strong potential for financial time-series forecasting and can effectively capture important sequential patterns in stock market data.
View the results as a methodological contribution rather than direct evidence of practical investment value, given the modest trend-classification accuracy and the lack of trading back testing, transaction costs, or risk-adjusted performance measures.
The results indicate that self-supervised representation learning can serve as an effective and scalable substitute for manual feature engineering in finance time-series forecasting.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.