206 citations · 207 across the 8 of their papers we have counts for
5 papers · 1 filter
Multi-Horizon Time Series Forecasting of non-parametric CDFs with Deep Lattice Networks
Niklas Erdmann, Lars Bentsen, Roy Stenbro +3
Probabilistic forecasting is not only a way to add more information to a prediction of the future, but it also builds on weaknesses in point prediction. Sudden changes in a time se…
Reinforcement Learning for Pollution Detection in a Randomized, Sparse and Nonstationary Environment with an Autonomous Underwater Vehicle
Sebastian Zieglmeier, Niklas Erdmann, Narada D. Warakagoda
Reinforcement learning (RL) algorithms are designed to optimize problem-solving by learning actions that maximize rewards, a task that becomes particularly challenging in random an…
Deep and Probabilistic Solar Irradiance Forecast at the Arctic Circle
Niklas Erdmann, Lars Ø. Bentsen, Roy Stenbro +3
Solar irradiance forecasts can be dynamic and unreliable due to changing weather conditions. Near the Arctic circle, this also translates into a distinct set of further challenges.…
It is all Connected: A New Graph Formulation for Spatio-Temporal Forecasting
Lars Ødegaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro +1
With an ever-increasing number of sensors in modern society, spatio-temporal time series forecasting has become a de facto tool to make informed decisions about the future. Most sp…
Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures
Lars Ødegaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro +1
This study focuses on multi-step spatio-temporal wind speed forecasting for the Norwegian continental shelf. The study aims to leverage spatial dependencies through the relative ph…