Fusion Of SSA Mahjong Search and LSTM Model for Vegetable Sales and Pricing Research
DOI:
https://doi.org/10.54097/24hsnw44Keywords:
LSTM Model, Replenishment and Pricing Decision, SSA Sparrow Search Algorithm.Abstract
In supermarkets, vegetable products have a short shelf life, and their quality deteriorates over time, making it challenging to sell them the next day. To formulate rational sales and pricing decisions, this article first analyzes the relationship between sales and prices. Due to the unique nature of vegetable products, pricing may also be influenced by historical sales and pricing information, which can be analyzed through regression data.The study begins with data preprocessing using EMD decomposition and utilizes an LSTM model to predict various vegetable categories. Additionally, an intelligent optimization algorithm, the SSA Sparrow Search Algorithm, is employed to optimize sales forecasts for the upcoming week. Combining historical sales data and pricing information, a multivariate time series prediction is performed, again using the LSTM model, but leveraging Bayesian optimization to determine pricing for various vegetable categories in the upcoming week.This approach provides a pricing strategy that maximizes the benefits for supermarkets.
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