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
Worked on the Nixtla/neuralforecast repository to enhance in-sample prediction capabilities by introducing support for user-specified confidence levels and quantiles, enabling distribution-aware forecasts. Refactored the prediction generation process in Python and Jupyter Notebook to accommodate these new parameters, ensuring compatibility with various loss functions and deep learning models. Implemented robust error handling to prevent misconfigurations, particularly for unsupported scenarios such as multivariate models or conformal prediction intervals with in-sample predictions. This work focused on API development and time series forecasting, resulting in a more flexible and reliable prediction interface that supports advanced machine learning workflows without introducing new bugs.
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 peopleWork 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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