Introducing ProsperNN—a Python package for forecasting with neural networks
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 uncerta...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-11-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2481.pdf |
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| _version_ | 1846151346155159552 |
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| author | Nico Beck Julia Schemm Claudia Ehrig Benedikt Sonnleitner Ursula Neumann Hans Georg Zimmermann |
| author_facet | Nico Beck Julia Schemm Claudia Ehrig Benedikt Sonnleitner Ursula Neumann Hans Georg Zimmermann |
| author_sort | Nico Beck |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a4b25fa7413c478585ac94eaa2859fcd |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-a4b25fa7413c478585ac94eaa2859fcd2024-11-27T15:05:18ZengPeerJ Inc.PeerJ Computer Science2376-59922024-11-0110e248110.7717/peerj-cs.2481Introducing ProsperNN—a Python package for forecasting with neural networksNico BeckJulia SchemmClaudia EhrigBenedikt SonnleitnerUrsula NeumannHans Georg ZimmermannWe 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.https://peerj.com/articles/cs-2481.pdfPrice forecastingMacroeconomic forecastingFinancial forecastingSoftwareRecurrent neural networks |
| spellingShingle | Nico Beck Julia Schemm Claudia Ehrig Benedikt Sonnleitner Ursula Neumann Hans Georg Zimmermann Introducing ProsperNN—a Python package for forecasting with neural networks PeerJ Computer Science Price forecasting Macroeconomic forecasting Financial forecasting Software Recurrent neural networks |
| title | Introducing ProsperNN—a Python package for forecasting with neural networks |
| title_full | Introducing ProsperNN—a Python package for forecasting with neural networks |
| title_fullStr | Introducing ProsperNN—a Python package for forecasting with neural networks |
| title_full_unstemmed | Introducing ProsperNN—a Python package for forecasting with neural networks |
| title_short | Introducing ProsperNN—a Python package for forecasting with neural networks |
| title_sort | introducing prospernn a python package for forecasting with neural networks |
| topic | Price forecasting Macroeconomic forecasting Financial forecasting Software Recurrent neural networks |
| url | https://peerj.com/articles/cs-2481.pdf |
| work_keys_str_mv | AT nicobeck introducingprospernnapythonpackageforforecastingwithneuralnetworks AT juliaschemm introducingprospernnapythonpackageforforecastingwithneuralnetworks AT claudiaehrig introducingprospernnapythonpackageforforecastingwithneuralnetworks AT benediktsonnleitner introducingprospernnapythonpackageforforecastingwithneuralnetworks AT ursulaneumann introducingprospernnapythonpackageforforecastingwithneuralnetworks AT hansgeorgzimmermann introducingprospernnapythonpackageforforecastingwithneuralnetworks |