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English(EN) Integrable Elasticity via Neural Demand Potentials

新型神经网络模型预测零售需求和弹性

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种名为可积情景依赖需求网络（ICDN）的新型神经网络模型，用于多产品零售需求。该模型将对数需求学习为对数价格的函数，从而能够从学习到的需求曲面精确推导出弹性。将ICDN应用于Dominick's啤酒数据集，与现有基准相比，ICDN在样本外泛化能力和弹性估计稳定性方面均有所提高。 AI

影响 引入了一种新颖的经济建模神经网络方法，有望改善零售需求预测和弹性分析。

排序理由 该集群包含一篇详细介绍新模型及其在数据集上评估的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新型神经网络模型预测零售需求和弹性

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Carlos Heredia, Daniel Roncel ·

    通过神经需求势实现可积弹性

    arXiv:2605.22820v1 Announce Type: new Abstract: We propose the Integrable Context-Dependent Demand Network (ICDN), a demand-first neural model for multiproduct retail demand. The model learns log-demand as a smooth, context-conditioned function of log-prices, allowing elasticitie…

  2. arXiv cs.LG TIER_1 English(EN) · Daniel Roncel ·

    通过神经需求势实现可积弹性

    We propose the Integrable Context-Dependent Demand Network (ICDN), a demand-first neural model for multiproduct retail demand. The model learns log-demand as a smooth, context-conditioned function of log-prices, allowing elasticities to be derived exactly from the learned demand …

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