Introducing ProsperNN—a Python package for forecasting with neural networks - Nico Beck; Julia Schemm; Claudia Ehrig; Benedikt Sonnleitner; Ursula Neumann; Hans Georg Zimmermann (PeerJ Computer Science)
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.