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:2609.06656v1 Announce Type: cross
Abstract: Modern power systems are growing increasingly complex as they integrate diverse generation sources to meet rising demand, making accurate load foreca...
By Varsha Pendyala, Yiwei Fu, Weizhong Yan, Nurali Virani
District heating energy hubs require reliable heat load forecasts for efficient operational scheduling. Conventional forecasting workflows train system-specific models on historical data, which can become burdensome when networks change through new consumers, retrofits, or changing operating regimes.
arXiv:2608. 20024v1 Announce Type: new Abstract: District heating energy hubs require reliable heat load forecasts for efficient operational scheduling.
By Ben Spoek, Karim K. Ben Hicham, Kai Derzsi, Philipp Althaus, Alexander Mitsos, Dirk M\"uller
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.
By Qianyang Li, Xingjun Zhang, Shaoxun Wang, Tao Peng, Jia Wei
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: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
The paper introduces the first multi‑horizon benchmark for forecasting resale prices of second‑hand electronics, comparing eleven statistical and deep learning models on a large Polish marketplace dataset covering 100+ smartphone and laptop models from January 2022 to March 2025. Across horizons from 1 to 365 days, N‑BEATS consistently outperforms all statistical baselines, achieving an 8.51% MAPE at 365 days versus 14.94% for the best baseline—a 43% reduction—while all models converge to about 0.72% MAPE for short horizons. A single N‑BEATS model trained for the 365‑day horizon generalizes to all shorter horizons, eliminating the need for horizon‑specific models, and N‑BEATS and N‑HiTS show superior hyperparameter stability.
By Mateusz Buczy\'nski, Micha{\l} Wo\'zniak, Konrad Kaczy\'nski, Anna Wr\'oblewska, Sebastian Kuk
arXiv:2609.39789v1 Announce Type: cross
Abstract: Real-world time series forecasting systems operate under non-stationary data streams, where forecasting performance may degrade over time. Although r...
By Yeryeong Kwak, Yoo-Min Jung, Jonghun Park
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
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
arXiv:2604. 22328v2 Announce Type: replace-cross Abstract: Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operations.
By Marco Obermeier, Marco Pruckner, Florian Haselbeck, Andreas Zeiselmair