Abstract
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.
Paper Type
Poster
DOI
10.62036/ISD.2024.70
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.
Recommended Citation
Bartkowiak, M., Cyplik, P., Górecki, T. & Karolewski, A. (2024). Innovative Sales Forecasting: Utilizing Fuzzy Neural Networks for Enhanced Sales Prediction. In B. Marcinkowski, A. Przybylek, A. Jarzębowicz, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Harnessing Opportunities: Reshaping ISD in the post-COVID-19 and Generative AI Era (ISD2024 Proceedings). Gdańsk, Poland: University of Gdańsk. ISBN: 978-83-972632-0-8. https://doi.org/10.62036/ISD.2024.70