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Node-Level Financial Optimization in Demand Forecasting Through Dynamic Cost Asymmetry and Feedback Mechanism

Alessandro Casadei, Clemens Grupp, Sreyoshi Bhaduri, Lu Guo, Wilson Fung, Rohit Malshe, Raj Ratan, Ankush Pole, Arkajit Rakshit

TL;DR

The paper addresses node-level demand forecasting under asymmetric regret costs, proposing a per-node forecast adjustment that biases toward the cheaper error type. It defines a two-slope regret-cost function with slopes $-\hat{CPP}_{L}$ and $\hat{CPP}_{H}$ and optimizes a shifted base forecast via $\Delta$ to minimize the expected cost $\mathbb{E}[\hat{C}]$, while employing a dynamic self-regulation using the utilization $\mathcal{U}$ and a feedback loop on calibration and CPP-prediction errors. Data processing enhances predictive power through time-weighting and D-1 noise reduction, isolating WK-1 effects and yielding robust estimates of regret costs, with empirical results showing $5.1\mathrm{M}$ in annual EU savings (about 19.8% of the $25.7\mathrm{M}$ maximum under ideal adjustment). The approach provides a scalable, per-node framework that adapts to calibration drift and macro shifts, offering practical savings and a path to broader horizon integration and safety-timedelivery considerations. This work thus contributes a node-centric, self-regulating forecasting adjustment mechanism grounded in observed regret costs for improved last-mile efficiency.

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.

Node-Level Financial Optimization in Demand Forecasting Through Dynamic Cost Asymmetry and Feedback Mechanism

TL;DR

The paper addresses node-level demand forecasting under asymmetric regret costs, proposing a per-node forecast adjustment that biases toward the cheaper error type. It defines a two-slope regret-cost function with slopes and and optimizes a shifted base forecast via to minimize the expected cost , while employing a dynamic self-regulation using the utilization and a feedback loop on calibration and CPP-prediction errors. Data processing enhances predictive power through time-weighting and D-1 noise reduction, isolating WK-1 effects and yielding robust estimates of regret costs, with empirical results showing in annual EU savings (about 19.8% of the maximum under ideal adjustment). The approach provides a scalable, per-node framework that adapts to calibration drift and macro shifts, offering practical savings and a path to broader horizon integration and safety-timedelivery considerations. This work thus contributes a node-centric, self-regulating forecasting adjustment mechanism grounded in observed regret costs for improved last-mile efficiency.

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.
Paper Structure (10 sections, 19 equations, 5 figures, 2 tables)

This paper contains 10 sections, 19 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Data Processing
  4. Time-weighting
  5. Isolation of WK-1 Forecast-Driven Regret Cost through D-1 Noise Reduction
  6. Regret cost function definition
  7. Adjustment of Forecasting Error Probability to Reflect Regret Cost Asymmetry
  8. Application of dynamic self-regulation
  9. Financial results
  10. Future work

Figures (5)

  • Figure 1: Model's conceptual design for each node $s$ in the network.
  • Figure 2: Causal DAG - Representation of the causal relationship between D-1 and WK-1 forecasts on regret cost.
  • Figure 3: Regret cost function
  • Figure 4: Applying a positive forecast adjustment $\Delta$ based on $P(L)^*_s$ (red curve) starting from the error distribution of the base forecast (blue curve).
  • Figure 5: Minimum $\mathbb{E}[\hat{C}]=\mathbb{E}[\hat{C_L}]+\mathbb{E}[\hat{C_H}]$ with $\Delta=\Delta^*$