English(EN) Optimizing ARDL Models for Retail Sales Forecasting and Fair Pricing

新的ARDL模型将公平性融入零售定价策略

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-14 04:00

研究人员开发了一种新的零售动态定价方法，该方法包含公平性约束，以平衡盈利能力和消费者福利。该方法使用对数-对数自回归分布滞后（ARDL）模型来预测零售销售，并通过优化定价决策来最大化销售，同时遵守与消费者价格指数（CPI）挂钩的价格上限。该框架旨在通过确保价格不超过以CPI为锚定的上限来防止消费者被剥削，并使用模拟退火来寻找能够降低消费者成本但仍能实现销售目标的保守价格。研究还对预测准确性与ARIMA和SARIMA等传统模型进行了基准测试，并指出观察到的正名义弹性主要受通货膨胀影响。 AI

影响引入了一种将消费者公平性整合到动态定价中的新方法，可能影响零售策略和经济模型。

排序理由学术论文，详细介绍了零售销售预测和定价的新方法。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 学术论文，详细介绍了零售销售预测和定价的新方法。[lever_c_demoted from research: ic=1 ai=0.7]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, product, policy; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 58 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

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arXiv cs.LG TIER_1 English(EN) · Sujay Uday Rittikar · 2026-07-14 04:00

优化ARDL模型用于零售销售预测和公平定价

arXiv:2607.09956v1 Announce Type: new Abstract: 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 co…

本文如何被排名

报道来源 [1]

优化ARDL模型用于零售销售预测和公平定价

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