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Evidence record 3037 · automatically gathered

Optimizing ARDL Models for Retail Sales Forecasting and Fair Pricing

Pricing food products to balance profitability with consumer welfare is a central challenge for retailers. Dynamic pricing is widely used to maximize revenue, yet most pricing models optimize business objectives while overlooking consumer fairness. This paper studies the risk of consumer exploitation under dynamic food pricing in Canada and proposes a methodology that embeds fairness constraints directly into retail sales forecasting. We model total retail trade sales with a log--log Autoregress

Record details

Published: 10 July 2026
Source: arXiv fairness query
Category: Research
Topics: Bias & fairness
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (10 July 2026), “Optimizing ARDL Models for Retail Sales Forecasting and Fair Pricing,” evidence record 3037, https://ethics.ai/record/3037 (originally published by arXiv fairness query).

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