Bi-Directional Long Short-Term Memory Optimization by Improved Teaching-Learning Based Algorithm for Univariate Gold Price Forecasting

Critical operations of the global financial sector are significantly impacted by the volatility of the price of gold. Developing a robust forecasting model capable of recognizing patterns in the gold price dynamics may significantly reduce investment risks and enable considerable profits. This manuscript introduces an inventive deep learning forecasting system rested on the Bi-directional Long Short-term (BiLSTM) neural network that exploits the potential of these networks to apprehend short-term as well as long-term dependencies. This study developed a unique improved Teaching-learning Based (ITLB) method utilized to optimize the hyperparameter selection procedure since the performance of neural networks is heavily dependent upon the combination of adequate control parameters. Variation Mode Decomposition (VMD) was also used to discover trends in the gold price data prior to being submitted as input to the BiLSTM. A set of experiments has been conducted and the suggested model was compared to several cutting-edge metaheuristic algorithms. Overall results illustrate that the introduced BiLSTM-VMD-ITLB approach achieved superior forecasting results in predicting gold price fluctuations.