Publications (6)
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…
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…
TAT: Temporal-Aligned Transformer for Multi-Horizon Peak Demand Forecasting
Zhiyuan Zhao, Sitan Yang, Kin G. Olivares +5
Multi-horizon time series forecasting has many practical applications such as demand forecasting. Accurate demand prediction is critical to help make buying and inventory decisions…
GEANN: Scalable Graph Augmentations for Multi-Horizon Time Series Forecasting
Sitan Yang, Malcolm Wolff, Shankar Ramasubramanian +3
Encoder-decoder deep neural networks have been increasingly studied for multi-horizon time series forecasting, especially in real-world applications. However, to forecast accuratel…
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…
SPADE-S: A Sparsity-Robust Foundational Forecaster
Malcolm Wolff, Matthew Li, Ravi Kiran Selvam +11
Despite significant advancements in time series forecasting, accurate modeling of time series with strong heterogeneity in magnitude and/or sparsity patterns remains challenging fo…