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

Accurate retail sales forecasting is crucial for understanding customer demands, handling inventories, and optimizing business strategies. Conventional forecasting approaches struggle to take into consideration both linear and nonlinear transformations in the time series data, limiting the model's adaptability. Additionally, an effective business strategy is essential for improving overall company revenue, depending on insights from precise forecasting. In order to address these shortcomings, the Meta-Learning Enhanced Learnable Long Short-Term Memory network (Meta-LLSTM) is proposed for effective retail sales forecasting and generalization. The model applies meta learning for quickly adapting with enhanced forecasting under diverse operational conditions. On the contrary, the Multiple-Parameter Exponential Linear Unit (MPELU) introduces learnable parameters, enabling the model to handle both linear and nonlinear transformations to effectively forecast retail sales in business strategies. To improve retail sales forecasting, the following support modules are used in this study: Recency, Frequency, Monetary and Diversity (RFMD) to analyze customer sales information, K-means based customer segmentation, and Adaptive Inventory Correction (AIC) for information on inventories. Additionally, the metrics of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R-squared), Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the Meta-LLSTM. The proposed model achieves an RMSE of 1.003 which is less than that of the state-of-the-art classifiers such as Auto Encoder (AE), Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Recurrent Neural Network (RNN). Specifically, the RMSE of Meta-LLSTM is 16.97% less than the state of art approach (RNN), rendering it more effective than the existing models.