arXiv Machine Learning

FreshRetailNet-50K: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail

arXiv:2505. 16319v5 Announce Type: replace Abstract: Accurate demand estimation is critical for the retail business in guiding the inventory and pricing policies of perishable products.

arXiv Machine Learning

Jun 10

FreshRetailNet-LT: A Stockout-Annotated Censored Demand Dataset for Latent Demand Recovery and Forecasting in Fresh Retail

arXiv:2505. 16319v3 Announce Type: replace Abstract: Accurate demand estimation is critical for the retail business in guiding the inventory and pricing policies of perishable products.

By Yangyang Wang, Jiawei Gu, Li Long, Xin Li, Li Shen, Zhouyu Fu, Xiangjun Zhou, Xu Jiang

benchmarks safety

arXiv Machine Learning

Sep 15

Accuracy Is Not Service: A Decision-Aware Benchmark for Intermittent-Demand Forecasting

arXiv:2609.13840v1 Announce Type: new Abstract: A contract-logistics spare-parts operator is paid on order-level service: an order counts only if every requested line is fulfilled, yet forecasters ar...

By Joo Ern Chin, Shih-Fen Cheng, Aldy Gunawan

benchmarks safety

arXiv Machine Learning

Aug 27

CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition

CEDAR is a two‑stage framework for demand forecasting that incorporates planned actions and external event signals. Stage I uses an Action‑Interleaved Transformer to model controllable state transitions under interventions, while Stage II applies a Residual Correction Module that aligns event descriptions with product context using LLM‑assisted text representations. Experiments on a large Alibaba 1688 dataset show that CEDAR improves simulation accuracy over traditional time‑series forecasting baselines and benefits real‑world budget planning.

By Junjie Meng, Ranxu Zhang, Zi-an Zhang, Shujun Liu, Xiaoning Qi, Xiaozhou Xu, Yanyong Zhang, Hui Xiong, Chao Wang

llms

arXiv AI

Jul 22

Censoring-Aware In-Context Learning for Generalized Supplier Lead Time Estimation in Supply Chain Planning

arXiv:2607. 18530v1 Announce Type: cross Abstract: Supplier lead time forecasting is a central input to material requirements planning, inventory optimization, and supply chain risk management.

By Christopher Wang, Sebastien Ouellet, Behrouz Haji Soleimani, Ali Etemad

llms

arXiv AI

Jul 21

A Predict-then-Correct Loop Based on Few-Shot Continuous Contextual Bandit for Demand Forecasting

arXiv:2607. 16354v1 Announce Type: cross Abstract: Retail demand forecasting remains difficult when demand shifts faster than static forecasting models can be retrained, especially in early demand cycles where newly observed labels are sparse.

By Zhiwei Lei, Benedict Jun Ma, Ilya Jackson

reinforcement-learning benchmarks

arXiv Machine Learning

Sep 15

Are Gradient Boosting Models Suitable for Intermittent Demand Forecasting?

arXiv:2609.14718v1 Announce Type: new Abstract: Demand forecasting is critical in modern industry, offering opportunities to reduce costs and gain competitive advantage through improved inventory man...

By Vladislav Kislinskii, Mazhar Hameed

arXiv Machine Learning

Jul 21

Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables

arXiv:2603. 15802v2 Announce Type: replace Abstract: In many time series forecasting settings, the target time series is accompanied by exogenous covariates, such as promotions and prices in retail demand; temperature in energy load; calendar and holiday indicators for traffic or sales; and grid load or fuel costs in electricity pricing.

By Andres Potapczynski, Ravi Kiran Selvam, Tatiana Konstantinova, Malcolm Wolff, Kin G. Olivares, Ruijun Ma, Michael W. Mahoney, Andrew Gordon Wilson, Boris N. Oreshkin, Dmitry Efimov

benchmarks

arXiv Machine Learning

Jul 16

Accuracy-Preserving Stability Regularization for Large-Scale Retail Demand Forecasting

arXiv:2607. 13331v1 Announce Type: new Abstract: Retail demand forecasts are reused across replenishment, capacity, labor, and transportation planning cycles.

By Jize Li, Jiani He, Dishu Yang, Dingyan Shang, Jingjing Liu, Shiqi Huang

rag

arXiv Machine Learning

Sep 1

Nonparametric Contextual Pricing and Inventory Learning under Censored Demand

arXiv:2608.30944v1 Announce Type: new Abstract: In online retailing, when a product sells out, a retailer often sees only the units sold, not how many customers would have bought it had inventory bee...

By Zean Han, Jing Liang, Ruihan Lin, Zezhen Ding, Jiheng Zhang

arXiv Machine Learning

Jun 2

ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks

arXiv:2605. 12768v2 Announce Type: replace-cross Abstract: Open time-series forecasting (TSF) benchmarks cover retail, energy, weather, and traffic, but supply-chain logistics remains underserved.

By Zhizhen Zhang, Hyemin Gu, Benjamin J. Zhang, Daniel Elenius, Michael Tyrrell, Theo J. Bourdais, Houman Owhadi, Markos A. Katsoulakis, Tuhin Sahai

llms benchmarks

arXiv Machine Learning

Jul 14

Optimizing ARDL Models for Retail Sales Forecasting and Fair Pricing

arXiv:2607. 09956v1 Announce Type: new Abstract: Pricing food products to balance profitability with consumer welfare is a central challenge for retailers.

By Sujay Uday Rittikar

benchmarks safety

arXiv AI

Aug 14

Demand Transfer Estimation at Scale via Restricted Logit Modeling

arXiv:2608. 12680v1 Announce Type: cross Abstract: Item demand forecasting is an integral component of store assortment optimization.

By Lakshya Garg, Deep Narayan Mishra, Swapnil Yadav, Haoan Wang, Sujal Alugubelli, Karthik Kumaran, Anupriya Sharma

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