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Integrable Elasticity via Neural Demand Potentials

arXiv 2605.22820 ↗First posted 21 May 2026cs.LGcited by 0

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Abstract (arXiv)

We propose the Integrable Context-Dependent Demand Network (ICDN), a demand-first neural model for multiproduct retail demand. ICDN learns log-demand as a smooth, context-conditioned function of log-prices, allowing elasticities to be derived exactly from the learned demand surface. Across three retail datasets, the model achieves competitive out-of-sample prediction while producing analytically tractable, and economically regularized own- and cross-price responses.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 41 references that are in the map. First posted on arXiv 21 May 2026.

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References · 6

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  3. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,013
  4. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 761
  5. 2017A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Predictioncited by 245
  6. 2021Estimating smooth and sparse neural receptive fields with a flexible spline basiscited by 1

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