3 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.LG2024
SPADE Split Peak Attention DEcomposition
Malcolm Wolff, Kin G. Olivares, Boris Oreshkin +8
Demand forecasting faces challenges induced by Peak Events (PEs) corresponding to special periods such as promotions and holidays. Peak events create significant spikes in demand f…
cs.LG2024★ 2 cited
F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data
Zexing Xu, Linjun Zhang, Sitan Yang +4
Demand prediction is a crucial task for e-commerce and physical retail businesses, especially during high-stake sales events. However, the limited availability of historical data f…
cs.LG2022★ 3 cited
MQRetNN: Multi-Horizon Time Series Forecasting with Retrieval Augmentation
Sitan Yang, Carson Eisenach, Dhruv Madeka
Multi-horizon probabilistic time series forecasting has wide applicability to real-world tasks such as demand forecasting. Recent work in neural time-series forecasting mainly focu…