Forecasting with sktime
A unified Python framework for machine learning with time series, offering scikit-learn compatible tools for forecasting, classification, clustering, and more.
What is Forecasting with sktime?
sktime is a Python library that provides a unified framework for machine learning with time series data. It solves the problem of fragmented tooling by offering a consistent interface for tasks like forecasting, classification, clustering, and anomaly detection, all with scikit-learn compatibility.
Data scientists, machine learning engineers, and researchers working with time series data who need a comprehensive, interoperable toolkit for analysis and modeling.
Developers choose sktime for its unified API across multiple time series tasks, seamless integration with the scikit-learn ecosystem, and rich model composition capabilities, reducing the need to learn disparate libraries.
Overview
A unified framework for machine learning with time series
Use Cases
Best For
- Building end-to-end time series forecasting pipelines
- Classifying time series data with scikit-learn compatible models
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