3 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.LG2023
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…
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…