Nixtla/neuralforecast
Jun 2025 – Jun 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
API DevelopmentDeep LearningMachine LearningPythonSoftware EngineeringTime Series Forecasting
Jan Overgoor
PROFILE
Jan Overgoor
Jan Overgoor enhanced the Nixtla/neuralforecast repository by developing distribution-aware in-sample prediction capabilities, allowing users to specify confidence levels and quantiles directly within the predict_insample function. He refactored the prediction generation logic in Python and Jupyter Notebook to support these new parameters, ensuring compatibility with a range of loss functions and deep learning models. His work included robust error handling to prevent unsupported configurations, such as multivariate models or conformal prediction intervals, from causing misconfigurations. This feature deepened the library’s support for time series forecasting, reflecting a thoughtful approach to API development and software engineering best practices.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
519
Activity Months1
Your Network
11 peopleShared Repositories
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Jing-Qiang (JQ) GohMember
José MoralesMember
Lemuel LeeMember
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SaulMember
Tyler BlumeMember
Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025Concise monthly summary for Nixtla/neuralforecast – June 2025: Distribution-aware in-sample predictions and robustness improvements.
1 Commits • 1 Features
Jun 1, 2025Concise monthly summary for Nixtla/neuralforecast – June 2025: Distribution-aware in-sample predictions and robustness improvements.
June 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
API DevelopmentDeep LearningMachine LearningPythonSoftware EngineeringTime Series Forecasting
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