A1780
Title: Deep learning for energy forecasting: A benchmark
Authors: Alexandru-Victor Andrei - Bucharest University of Economic Studies (Romania) [presenting]
Daniel Traian Pele - Bucharest University of Economic Studies, Institute for Economic Forecasting, Romanian Academy (Romania)
Abstract: A public data set related to energy is identified that facilitates forecasting. Using this data set, alternative (old vs. new) forecasting methods are compared in terms of how well they predict the time series. Several user-friendly libraries like PyTorch forecasting, time series library (TSlib), etc. offer access to several recently introduced forecasting methods.
