Year: 2024
Type: software-paper
Abstract: We present the package prosper_nn, that provides four neural network architectures dedicated to time series forecasting, implemented in PyTorch. In addition, prosper_nn contains the first sensitivity analysis suitable for recurrent neural networks (RNN) and a heatmap to visualize forecasting uncertainty, which was previously only available in Java. These models and methods have successfully been in use in industry for two decades and were used and referenced in several scientific publications. However, only now we make them publicly available on GitHub, allowing researchers and practitioners to benchmark and further develop them. The package is designed to make the models easily accessible, thereby enabling research and application in various fields like demand and macroeconomic forecasting.
Source: PeerJ Computer Science
Authors: Nico Beck, Julia Schemm, Claudia Ehrig, Benedikt Sonnleitner, Ursula Neumann, Hans Georg Zimmermann
Institution: Fraunhofer Institute for Integrated Circuits
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,395
Awards: G320941915, G8010892114
OpenAlex ID: W4404698259