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Temporal Fusion Transformer for Multi-Horizon Probabilistic Forecasting of Weekly Retail Sales

Author

Listed:
  • Santhi Bharath Punati
  • Sandeep Kanta
  • Udaya Bhasker Cheerala
  • Madhusudan G Lanjewar
  • Praveen Damacharla

Abstract

Accurate multi-horizon retail forecasts are critical for inventory and promotions. We present a novel study of weekly Walmart sales (45 stores, 2010--2012) using a Temporal Fusion Transformer (TFT) that fuses static store identifiers with time-varying exogenous signals (holidays, CPI, fuel price, temperature). The pipeline produces 1--5-week-ahead probabilistic forecasts via Quantile Loss, yielding calibrated 90\% prediction intervals and interpretability through variable-selection networks, static enrichment, and temporal attention. On a fixed 2012 hold-out dataset, TFT achieves an RMSE of \$57.9k USD per store-week and an $R^2$ of 0.9875. Across a 5-fold chronological cross-validation, the averages are RMSE = \$64.6k USD and $R^2$ = 0.9844, outperforming the XGB, CNN, LSTM, and CNN-LSTM baseline models. These results demonstrate practical value for inventory planning and holiday-period optimization, while maintaining model transparency.

Suggested Citation

  • Santhi Bharath Punati & Sandeep Kanta & Udaya Bhasker Cheerala & Madhusudan G Lanjewar & Praveen Damacharla, 2025. "Temporal Fusion Transformer for Multi-Horizon Probabilistic Forecasting of Weekly Retail Sales," Papers 2511.00552, arXiv.org.
    • Handle: RePEc:arx:papers:2511.00552
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    References listed on IDEAS

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    1. Hamid Ahaggach & Lylia Abrouk & Eric Lebon, 2024. "Systematic Mapping Study of Sales Forecasting: Methods, Trends, and Future Directions," Forecasting, MDPI, vol. 6(3), pages 1-31, July.
    2. Borah, Abhishek & Rutz, Oliver, 2024. "Enhanced sales forecasting model using textual search data: Fusing dynamics with big data," International Journal of Research in Marketing, Elsevier, vol. 41(4), pages 632-647.
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