Shranjeno v:
| Main Authors: | , , , |
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| Format: | Recurso digital |
| Jezik: | |
| Izdano: |
Zenodo
2026
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| Teme: | |
| Online dostop: | https://doi.org/10.5281/zenodo.19878507 |
| Oznake: |
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- This paper presents the end-to-end implementation terms. XGBoost exploits the explicit lag and festival columns of a demand forecasting system for a regional FMCG distributor through regularized tree splits and achieves sub-5 ms inference. in Maharashtra, India. The system comprises a Node.js/Express REST API backed by MongoDB, a Python Flask ML sidecar, TFT routes known future inputs—festival calendar, quarter and a React dashboard. Three machine learning models—Long indicators—through a dedicated encoder, enabling multi-head Short-Term Memory (LSTM), XGBoost, and Temporal Fusion self-attention over prior-year festival quarters. All three share Transformer (TFT)—were trained on real SKU-level quarterly the same feature set, so accuracy differences are attributable sales data. The ML pipeline covers outlier clipping, categorical to architecture alone. encoding, lag feature construction, festival-proximity scoring, and chronological train/validation/test splitting. On the held-out B. Contributions test set, LSTM achieved RMSE = 4,457.49 and MAPE = 18.34%; XGBoost achieved RMSE = 3,526.88 and MAPE = 14.87%; TFT Three aspects distinguish this work. First, the dataset is produced RMSE = 724.44, MAE = 381.67, MAPE = 6.32%, and operational rather than synthetic, carrying real-world noise, WAPE = 4.50%. The backend exposes authenticated REST end- return transactions, and irregular festival timing. Second, fes- points for CSV ingestion, feature preprocessing, model dispatch, and forecast retrieval. This paper documents each implementa- tival proximity is encoded as a continuous score rather than a tion layer—repository structure, data pipeline, model design, API binary flag, capturing the gradual pre-festival demand ramp- routes, and deployment strategy—as a reproducible blueprint for Third, the full system—preprocessing, training, inference, distributor-scale forecasting.