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Related Experiment Videos

Enterprise retail price prediction method based on improved HPO-LSTM algorithm.

Wei Zhu1

  • 1Jining College, Qufu, China.

Plos One
|January 2, 2026
PubMed
Summary

This study introduces an enhanced Hunter-Prey optimization-long short-term memory algorithm for accurate retail price prediction. The improved model significantly boosts prediction accuracy, reducing costs and increasing profits for businesses.

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Area of Science:

  • Economics
  • Computer Science
  • Artificial Intelligence

Background:

  • Accurate price prediction is crucial for enterprise success in market economies.
  • Current methods may lack the precision needed for optimal inventory management and profit maximization.

Purpose of the Study:

  • To develop an advanced optimization algorithm for precise retail price prediction.
  • To enhance resource allocation, economic efficiency, and business profitability.

Main Methods:

  • Integration of Hunter-Prey optimization algorithm with long short-term memory (LSTM) networks.
  • Incorporation of attention mechanisms and Q-learning for model optimization.

Main Results:

  • The proposed algorithm demonstrated superior prediction accuracy (RMSE: 0.48, MAE: 0.20) compared to control models.

Related Experiment Videos

  • Achieved high accuracy (0.972) and recall (0.921) post-convergence.
  • Led to significant reductions in inventory costs (15.2%) and promotion costs (20.3%), with increased sales revenue (15.4%) and profits (20.4%).

    • Conclusions:

    • The enhanced algorithm offers a robust and adaptable tool for retail price prediction.
    • Provides enterprises with improved inventory management, reduced waste, and enhanced market competitiveness.