arXiv Machine Learning

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.

arXiv Machine Learning

Jun 19

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.

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

benchmarks safety

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

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

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 AI

5d ago

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

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

Hugging Face Trending Papers

Jun 8

FAME: Forecastability-Aware Mixture of Experts for Heterogeneous Time Series Forecasting

Large-scale retail and industrial forecasting systems contain many heterogeneous time series whose lifecycle, sparsity, volatility, seasonality, spectral patterns, and contextual sensitivity differ substantially. A single forecasting model rarely performs well across all regimes, while dense ensembles increase inference cost and provide limited insight into expert suitability.

efficiency benchmarks

arXiv Machine Learning

Jun 17

Predictive Analytics in E-Commerce for CustomerBehavior Forecasting using hybrid Ret-DNN withXGBoost Model

arXiv:2606. 17931v1 Announce Type: new Abstract: In recent years, electronic (E) commerce services have rapidly increased in the daily lives of people, which helpsthem to purchase products online.

By Degala Pushpa Sri, Mayank Atreya, Lakshmi. H, Navin Chhibber, Mukesh Soni

arXiv AI

Jun 9

Integrating Deep Learning Demand Forecasting with Multi-Objective Optimization for Circular Coffee Supply Chains: A Data-Driven Framework for Cost, Emissions, and Freshness Management

arXiv:2606. 08314v1 Announce Type: new Abstract: The coffee supply chain is one of the most complex agri-food networks, marked by geographically dispersed production, multi-tier coordination, and high sensitivity to quality and freshness.

By Ger\c{c}ek Budak (Department of Industrial Engineering, Ankara Y{\i}ld{\i}r{\i}m Beyaz{\i}t University, Ke\c{c}i\"oren, Ankara 06010, T\"urkiye), Faraz Gholamzadeh Gharehgheshlaghi (Department of Industrial Engineering, Ankara Y{\i}ld{\i}r{\i}m Beyaz{\i}t University, Ke\c{c}i\"oren, Ankara 06010, T\"urkiye), Melika Barjesteh Vaezi (Department of Kinesiology and Sport Management, Texas Tech University, Lubbock, TX, United States), Ahmad Gholizadeh Lonbar (Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL, USA)

multimodal benchmarks

arXiv Machine Learning

Jun 3

FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance

arXiv:2606. 03184v1 Announce Type: cross Abstract: Financial forecasting is difficult due to low signal-to-noise ratios, latent factors, heavy tails, regime shifts, and jumps.

By Jiaze Sun, Kelvin J. L. Koa, Ruiyang Ni, Yize Liu, Haonan Chen, Ke-Wei Huang

llms multimodal benchmarks safety

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