2 papers
cs.LG2026
FDN: Interpretable Spatiotemporal Forecasting with Future Decomposition Networks
Nicholas Majeske, Ariful Azad
Spatiotemporal systems comprise a collection of spatially distributed yet interdependent entities each generating unique dynamic signals. Highly sophisticated methods have been pro…
cs.LG2021
Inductive Predictions of Extreme Hydrologic Events in The Wabash River Watershed
Nicholas Majeske, Bidisha Abesh, Chen Zhu +1
We present a machine learning method to predict extreme hydrologic events from spatially and temporally varying hydrological and meteorological data. We used a timestep reduction t…