优化自回归分布滞后模型用于零售销售预测和公平定价
Optimizing ARDL Models for Retail Sales Forecasting and Fair Pricing
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中文总结 AI 辅助
研究加拿大动态食品定价中消费者被剥削风险，提出将公平约束嵌入零售销售预测的方法，用ARDL模型结合线性规划和模拟退火解决定价问题，对比多种基准模型验证效果，构建出注重公平的定价框架。
中文摘要 AI 辅助
为食品定价以平衡盈利能力和消费者福利是零售商面临的核心挑战。动态定价虽广泛用于最大化收益，但多数定价模型忽视消费者公平性。本文研究加拿大动态食品定价中消费者被剥削的风险，并提出将公平约束直接嵌入零售销售预测的方法。用对数-对数自回归分布滞后（ARDL）模型模拟总零售贸易销售，将定价问题设为在以消费者价格指数（CPI）为锚定的价格边界下最大化预测销售。通过线性规划（LP）和模拟退火（SA）在单产品和多产品配置下解决此问题。关键发现是拟合的名义弹性为正，无约束的销售最大化者会将价格推至上限，而CPI上限可防止此情况。模拟退火则确定保守的内部价格，降低消费者成本同时满足销售目标。通过与朴素、季节性朴素、ARIMA和SARIMA基准对比预测准确性，结果是一个透明的、注重公平的定价框架。
英文摘要
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 Autoregressive Distributed Lag (ARDL) specification, in which the coefficient on a product price is a sales elasticity, and pose the pricing problem as maximizing forecast sales subject to price bounds anchored to the Consumer Price Index (CPI). We solve this problem with both Linear Programming (LP) and Simulated Annealing (SA), under single-product and multi-product configurations. A key finding is that the fitted nominal elasticities are positive. As a result, an unconstrained sales-maximizer would push every price to its upper bound, and the CPI ceiling is the safeguard that prevents this. Simulated Annealing instead settles on conservative, interior prices that lower consumer cost while still meeting the sales target. We benchmark forecast accuracy against naive, seasonal-naive, ARIMA, and SARIMA baselines, and a CPI-deflated re-specification shows that the positive nominal elasticities are largely an inflation-driven artifact. The result is a transparent, fairness-aware pricing framework.
发表机构
- The University of Winnipeg（温尼伯大学）
机构由 AI 辅助整理，请以论文原文为准。
