{"ID":2847269,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.00552","arxiv_id":"2511.00552","title":"Temporal Fusion Transformer for Multi-Horizon Probabilistic Forecasting of Weekly Retail Sales","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.","short_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 pipe...","url_abs":"https://arxiv.org/abs/2511.00552","url_pdf":"https://arxiv.org/pdf/2511.00552v1","authors":"[\"Santhi Bharath Punati\",\"Sandeep Kanta\",\"Udaya Bhasker Cheerala\",\"Madhusudan G Lanjewar\",\"Praveen Damacharla\"]","published":"2025-11-01T13:34:29Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"econ.GN\"]","methods":"[\"Transformer\",\"Convolutional Neural Network\"]","has_code":false}