arXiv:2607. 13331v1 Announce Type: new Abstract: Retail demand forecasts are reused across replenishment, capacity, labor, and transportation planning cycles.
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.
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...
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...
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.
arXiv:2606. 08896v1 Announce Type: new Abstract: Large-scale retail and industrial forecasting systems contain many heterogeneous time series whose lifecycle, sparsity, volatility, seasonality, spectral patterns, and contextual sensitivity differ substantially.
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.
The paper introduces fable.intermittent, an R package that consolidates various probabilistic forecasting methods for intermittent time series within the fable framework, enabling streamlined fitting and evaluation across multiple datasets. It also presents TWEES, a new exponential smoothing model using a Tweedie predictive distribution, and releases tweedieDistr, a faster implementation of the Tweedie distribution. The authors evaluate these tools on four datasets provided with the package.
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.
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.
The paper investigates whether forecasting-model selection should be treated as a context-dependent process rather than a universal one. It compares five selection mechanisms across 24 optimized models, nine datasets, and various training-testing partitions and horizons, finding that no single selector dominates in all conditions. The results show that selector performance varies with demand pattern, data availability, and horizon, suggesting a context-dependent approach is more appropriate.
The paper introduces two methods for modelling aggregated supply and demand curves in the EPEX SPOT Day‑Ahead market. The first is a low‑dimensional parametric approach that produces deterministic point forecasts using plateau levels, elastic‑region boundaries, polynomial coefficients, and XGBoost. The second is a high‑dimensional generative approach based on conditional Denoising Diffusion Probabilistic Models that samples plausible curves from price arrivals and volume‑increment marks, enabling analysis of price and volume sensitivity and price impact.
arXiv:2606. 13741v1 Announce Type: new Abstract: This paper presents the design, development, and implementation of a specialized forecast-then-optimize algorithmic pricing tool for sales campaigns in fashion e-commerce.