Node-Level Financial Optimization in Demand Forecasting Through Dynamic Cost Asymmetry and Feedback Mechanism
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866912783306588160 |
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| author | Casadei, Alessandro Grupp, Clemens Bhaduri, Sreyoshi Guo, Lu Fung, Wilson Malshe, Rohit Ratan, Raj Pole, Ankush Rakshit, Arkajit |
| author_facet | Casadei, Alessandro Grupp, Clemens Bhaduri, Sreyoshi Guo, Lu Fung, Wilson Malshe, Rohit Ratan, Raj Pole, Ankush Rakshit, Arkajit |
| contents | This work introduces a methodology to adjust forecasts based on node-specific cost function asymmetry. The proposed model generates savings by dynamically incorporating the cost asymmetry into the forecasting error probability distribution to favor the least expensive scenario. Savings are calculated and a self-regulation mechanism modulates the adjustments magnitude based on the observed savings, enabling the model to adapt to station-specific conditions and unmodeled factors such as calibration errors or shifting macroeconomic dynamics. Finally, empirical results demonstrate the model's ability to achieve \$5.1M annual savings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_19722 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Node-Level Financial Optimization in Demand Forecasting Through Dynamic Cost Asymmetry and Feedback Mechanism Casadei, Alessandro Grupp, Clemens Bhaduri, Sreyoshi Guo, Lu Fung, Wilson Malshe, Rohit Ratan, Raj Pole, Ankush Rakshit, Arkajit Machine Learning This work introduces a methodology to adjust forecasts based on node-specific cost function asymmetry. The proposed model generates savings by dynamically incorporating the cost asymmetry into the forecasting error probability distribution to favor the least expensive scenario. Savings are calculated and a self-regulation mechanism modulates the adjustments magnitude based on the observed savings, enabling the model to adapt to station-specific conditions and unmodeled factors such as calibration errors or shifting macroeconomic dynamics. Finally, empirical results demonstrate the model's ability to achieve \$5.1M annual savings. |
| title | Node-Level Financial Optimization in Demand Forecasting Through Dynamic Cost Asymmetry and Feedback Mechanism |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.19722 |