Deep Learning and Market Trends: Building Future Economic Forecasting Models

Economic market forecasting involves predicting future market conditions based on various data. By analyzing the market trends, organizations can make informed decisions, and strategize for potential economic shifts. Accurate forecasting is crucial for minimizing risks and maximizing opportunities. In this research, we aim to develop an innovative future economic forecasting model through a deep learning (DL) algorithm. For this, we propose a novel Drosophila Food Search-drivenGate Adjusted Long Short-Term Memory[Formula: see text]DFS-GA-LSTM[Formula: see text] for analyzing the market trends and providing extensive forecasting results of future economics. We obtained a dataset comprising historical financial market data and economic indicators from various media sources. It includes stock prices and economic growth metrics to train and evaluate the proposed model. Data normalization is utilized to pre-process the gathered raw information. Kernel principle component analysis (kernel-PCA) is utilized to remove the crucial features from the processed data. In our proposed model, the DFS optimization algorithm iteratively fine-tunes the GA-LSTM architecture to enhance forecasting accuracy. The established model is implemented using Python software. During the outcome analysis phase, we evaluate our model’s performance across several parameters like RMS (0.19), RMSE (0.009), MAE (0.00011), MSE 0.003, and MAPE (26.7%). In addition, we conducted comparative analyses using diverse existing methodologies. The result reveals the superiority and efficiency of the suggested future economic forecasting model.