Skip to content

Similar papers

Open access 2026

A Controlled Empirical Evaluation of Input Design and Temporal Architectures for Multi-Horizon Forecasting of Onion Wholesale Prices

Onion wholesale prices in South Korea exhibit substantial volatility due to seasonal production concentration, storage-dependent supply adjustments, and weather sensitivity, making accurate multi-horizon forecasting important for agricultural supply management. While previous research has predominantly focused on model...

Jia Yang, Younggu Park, Jin-Taek Seong · 0 citations
#machine learning Preprint Sep 2026

Are Gradient Boosting Models Suitable for Intermittent Demand Forecasting?

Demand forecasting is critical in modern industry, offering opportunities to reduce costs and gain competitive advantage through improved inventory management. However, forecasting becomes particularly challenging for products with intermittent demand, where demand occurs infrequently and time series contain many zero...

Vladislav Kislinskii, Mazhar Hameed · 0 citations
#machine learning Preprint Sep 2026

Beyond a Universal Forecasting Selector: Demand-Conditioned Model Selection across Demand Patterns and Horizons

Forecasting-model selection remains difficult in heterogeneous demand because the most suitable decision rule may vary with demand structure, data availability, and forecasting horizon. This study examines whether the selector itself should be treated as a context-dependent component of the forecasting process. Five se...

Adolfo González · 0 citations
Conference Aug 2026

AI-Enhanced Grouped Time-Series Forecasting with Weather and Holiday Effects

Operational wholesale forecasting depends on two levers: external drivers such as weather and holidays, and hierarchical coherence across customer groups. Their interaction with modern deep sequence models on multi-year business-to-business data is rarely measured with formal uncertainty quantification. We benchmark fo...

D. T. Ho, Man Duc Nguyen, Tamás Bérczes · 0 citations
#machine learning Preprint Oct 2026

Benchmarking Time Series Foundation Models for Load Forecasting Under Covariate Uncertainty

Accurate short-term load forecasting (STLF) is essential for the reliable and efficient operation of modern power systems. While time series foundation models (TSFMs) have recently demonstrated remarkable performance across a wide range of forecasting tasks, their effectiveness for STLF under realistic operational cond...

Tomas Kaljevic, I. Arzola, Yu Zhang · 0 citations
#machine learning Preprint Sep 2026

fable.intermittent: benchmarking probabilistic forecasting methods for intermittent time series

fable$.$intermittent, an R package that implements several probabilistic forecasting methods for intermittent series within the fable framework, and TWEES, a new exponential smoothing model with a Tweedie predictive distribution are introduced.

Stefano Damato, Lorenzo Zambon, G. Corani et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.