Logistics· 2026Q1
A Machine Learning Framework: E-Commerce Retail Sales Forecasting with External Weather Features
- 0citations
- Q1SCImago
- 2026year
Short summary
A Stacked LSTM model achieved the best sales forecast accuracy (MAE 28.50, RMSE 57.04) by integrating historical sales and weather data, though historical sales were the primary predictor.
AI-generated from the title and abstract; the full text is not read.
Key points
- A Stacked LSTM model achieved the highest accuracy in forecasting daily sales revenue.
- The best model achieved an MAE of 28.50, RMSE of 57.04, sMAPE of 5.82%, and R2 of 0.972.
- Historical sales data, especially the one-day lag, was identified as the most significant predictor.
- Weather variables provided supplementary predictive value beyond historical sales.
AI-generated from the title and abstract; the full text is not read.
Abstract
Background: Accurate sales forecasting supports inventory planning, resource allocation, revenue management, and operational decision-making. However, the incremental predictive value of external factors such as weather, beyond historical sales information, remains important to assess. This study evaluates an interpretable forecasting framework for daily-location sales revenue and examines the relative contributions of historical and weather information. Methods: Daily sales data were integrated with weather observations and temporal lag features. Random Forest, Extreme Gradient Boosting, CatBoost, and Stacked Long Short-Term Memory (LSTM) models were used for forecasting. Performance was assessed using mean absolute error, root mean squared error, symmetric mean absolute percentage error, and R2. Results: Stacked LSTM achieved the strongest overall test performance (MAE = 28.50, RMSE = 57.04, sMAPE = 5.82%, R2 = 0.972). Feature-importance analysis indicated that historical sales, particularly one-day sales lag, provided a stronger predictive signal than individual weather variables. Conclusions: Historical sales information was the primary predictive signal, while weather provided supplementary information. From a practical perspective, the framework can support revenue-oriented forecasting and help assess the additional value of incorporating weather information. This study also contributes to forecasting research by distinguishing the predictive role of historical sales patterns from the incremental contribution of external contextual variables.
The authors' abstract, as published at the source. Logistics, 2026 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Management Science and Operations Research
Management Science and Operations ResearchDecision Sciences