Sales Volume and Price Prediction Using Attention-Based RW-FN-BiLSTM Framework
Yuan Liu, Yilong Liu, Xiaoyan Guo, Xiaofei Han, Hongjing Shao · Applied and Computational Engineering · 2025
In the era of big data, accurate sales volume and price prediction is crucial for enterprises' market decision-making, inventory management, and pricing strategies. Traditional methods often fail to capture complex temporal patterns and nonlinear relationships in sales data, whereas deep learning offers promising solutions. This study proposes an Attention-based RW-FN-BiLSTM hybrid neural network to improve prediction accuracy. Using a 2015–2023 Hass Avocado Board dataset, covering multiple regions and key variables like the price and sales volume of conventional and organic avocados, the model integrates BiLSTM for long-term dependencies, RW-FN for feature extraction, and an Attention mechanism for adaptive weight allocation to key features. Experimental results show stable convergence, with predicted values closely following actual trends and an error distribution indicating strong generalization. While some deviations exist, further optimization in network structure and feature selection can enhance performance. This study validates the Attention-based RW-FN-BiLSTM model, providing enterprises with a data-driven approach to market trend analysis, inventory optimization, and pricing decisions, helping them make precise strategic choices in a competitive market environment.