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New neural network model predicts retail demand and elasticity

Researchers have developed a new neural network model called the Integrable Context-Dependent Demand Network (ICDN) for multiproduct retail demand. This model learns log-demand as a function of log-prices, enabling exact derivation of elasticities from the learned demand surface. Applied to the Dominick's beer dataset, ICDN demonstrated improved out-of-sample generalization and more stable elasticity estimates compared to existing benchmarks. AI

IMPACT Introduces a novel neural network approach for economic modeling, potentially improving retail demand forecasting and elasticity analysis.

RANK_REASON The cluster contains an academic paper detailing a new model and its evaluation on a dataset.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

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

    Integrable Elasticity via Neural Demand Potentials

    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 ·

    Integrable Elasticity via Neural Demand Potentials

    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 …