Innovative Sales Forecasting: Utilizing Fuzzy Neural Networks for Enhanced Sales Prediction

This study aims to improve retail sales forecasting using fuzzy neural networks (FNNs). Traditional methods often miss complex sales patterns. We use accuracy and loss metrics to apply FNNs to the Walmart sales dataset, comparing them to conventional time series models and advanced techniques like LightGBM and LSTM. Comprehensive data preprocessing ensures data quality. FNNs handle uncertainties and complex relationships better, outperforming traditional methods. The findings suggest that FNNs enhance forecasting accuracy, supporting informed decision-making in retail.