Node-Level Financial Optimization in Demand Forecasting Through Dynamic Cost Asymmetry and Feedback Mechanism
Abstract
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.
Cite
@article{arxiv.2512.19722,
title = {Node-Level Financial Optimization in Demand Forecasting Through Dynamic Cost Asymmetry and Feedback Mechanism},
author = {Alessandro Casadei and Clemens Grupp and Sreyoshi Bhaduri and Lu Guo and Wilson Fung and Rohit Malshe and Raj Ratan and Ankush Pole and Arkajit Rakshit},
journal= {arXiv preprint arXiv:2512.19722},
year = {2025}
}
Comments
Accepted to Amazon internal conference (AFSS). Now sharing with general public. This is submission is replacing a previous submission with the same title: the main paper is now submitted, while previously we submitted a summary