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Related Experiment Videos

Meta-LLSTM: meta-learning enhanced learnable LSTM for retail sales forecasting.

B S Suresh1, M Suresh2, Dae-Ki Kang3

  • 1Department of Management Studies, St. Peter's Institute of Higher Education and Research, Chennai, India.

Scientific Reports
|May 25, 2026
PubMed
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The Meta-Learning Enhanced Learnable Long Short-Term Memory network (Meta-LLSTM) improves retail sales forecasting accuracy. This novel approach enhances business strategies by effectively handling linear and nonlinear data transformations for better revenue.

Area of Science:

  • * Data Science and Machine Learning
  • * Business Analytics and Operations Research

Background:

  • * Accurate retail sales forecasting is vital for inventory management, demand prediction, and strategic business planning.
  • * Conventional forecasting models often fail to capture both linear and nonlinear dynamics in time series data, limiting their adaptability.
  • * Integrating precise forecasting with effective business strategies is key to enhancing company revenue.

Purpose of the Study:

  • * To introduce the Meta-Learning Enhanced Learnable Long Short-Term Memory network (Meta-LLSTM) for superior retail sales forecasting and generalization.
  • * To address limitations in existing models by incorporating meta-learning for rapid adaptation and a novel activation function for handling complex data transformations.
  • * To enhance forecasting accuracy and business strategy development through integrated analytical modules.
Keywords:
Business StrategiesCustomer SalesCustomer SegmentationLong Short-Term MemoryMultiple-Parameter Exponential Linear UnitRetail Sales Forecasting

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Main Methods:

  • * Development of the Meta-LLSTM model, utilizing meta-learning for adaptability and the Multiple-Parameter Exponential Linear Unit (MPELU) for handling linear/nonlinear transformations.
  • * Integration of Recency, Frequency, Monetary, and Diversity (RFMD) analysis for customer sales data.
  • * Implementation of K-means clustering for customer segmentation and Adaptive Inventory Correction (AIC) for inventory data management.

Main Results:

  • * The Meta-LLSTM model demonstrated superior performance, achieving a Root Mean Square Error (RMSE) of 1.003.
  • * Achieved a 16.97% reduction in RMSE compared to the Recurrent Neural Network (RNN), a leading state-of-the-art method.
  • * Outperformed other advanced models including Auto Encoder (AE), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU) in forecasting accuracy.

Conclusions:

  • * The Meta-LLSTM model offers a significant advancement in retail sales forecasting, outperforming existing state-of-the-art methods.
  • * The integration of meta-learning and advanced activation functions enables robust handling of complex time series data.
  • * The proposed model provides a foundation for more effective business strategies and improved revenue through enhanced forecasting accuracy.