NitroFE
A Python feature engineering engine that internally manages past dependent values for continuous calculation of time-based features.
What is NitroFE?
NitroFE is a Python feature engineering engine specifically designed for time-series data. It provides a variety of modules that internally manage past dependent values, enabling continuous calculation of features like moving averages, weighted windows, and technical indicators. This solves the challenge of externally saving state and implementing logic when moving feature pipelines from training to production.
Data scientists and machine learning engineers working with time-series data who need to create and deploy stateful feature engineering pipelines, particularly those transitioning models from research to production environments.
Developers choose NitroFE because it abstracts away the complexity of state management for time-dependent features, offering a production-ready solution with a rich library of pre-built indicators and window functions, reducing boilerplate code and deployment hurdles.
Overview
NitroFE is a Python feature engineering engine which provides a variety of modules designed to internally save past dependent values for providing continuous calculation.
Use Cases
Best For
- Creating rolling window features for time-series forecasting models
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