GA-FCFNN: A new forecasting method combining feature selection methods and feedforward neural networks using genetic algorithms

In the modern landscape, the fusion of forecasting and computational intelligence empowers organizations to extract invaluable insights from vast datasets, facilitating informed decision-making, swift adaptation to market dynamics, and the enhancement of competitiveness, ultimately fostering innovation. Notably, forecasting has recently garnered significant attention, particularly within the realm of big data. This modern terrain presents a dual challenge: effectively distilling essential insights from complex data and optimizing data utilization. To address this challenge, we introduce a pioneering forecasting model (FM) engineered to excel in both information extraction and data utilization. Our approach commences with a formula designed to compute feature similarity, leveraging trend change data. These similarity metrics, applied to features and their relationships with labels, inform our feature selection process. This method integrates spatial and temporal correlations among features, fostering robust interconnections within the selected subset. Subsequently, we employ a feedforward neural network (FNN) to optimize feature subsets based on test error. While the FM trained with the optimal feature subset exhibits enhanced performance, it still shows some deviation from actual values in initial forecasting results (FORs). To mitigate these discrepancies, we implement a data utilization strategy. We refine the initial FORs by considering the contribution of each feature subset to them, thereby reducing disparities between forecasts and actual values. Model parameters are fine-tuned using a genetic algorithm (GA). Finally, we conduct an experimental analysis, comparing our FM to seven similar models using publicly available datasets. Our experimental results consistently demonstrate that our FM achieves high forecasting performance (FOP).