Sktime
Sktime is an open-source Python library that offers a unified framework for machine learning tasks involving time series data. It supports multiple learning tasks such as forecasting, classification, clustering, and regression through a consistent and composable API modeled after scikit-learn. The library enables users to build composite models using pipelines, ensembles, hyperparameter tuning, and task reduction, and it supports both univariate and multivariate time series data. Sktime operates primarily on in-memory data structures based on pandas and NumPy, targeting medium-sized datasets. The framework includes dedicated time series algorithms rather than relying solely on adaptations of general-purpose methods. It also provides hierarchical forecasting capabilities and tools for fair model assessment and benchmarking. Sktime is designed for Python developers and data scientists familiar with scikit-learn, as well as researchers and practitioners working on various time series problems. The project is actively maintained and distributed under an open-source MIT license.
Sktime is a Python library providing a unified, scikit-learn-like API for multiple time series machine learning tasks including forecasting, classification, clustering, and regression.
Time Series Forecasting
Building and validating forecasting models for univariate or multivariate time series data using dedicated algorithms and hierarchical forecasting.
Time Series Classification
Applying classification algorithms to time series data with a consistent API and the ability to compose models via pipelines and ensembles.
Composite Model Development
Creating complex workflows involving pipelines, ensembling, hyperparameter tuning, and task reduction for various time series learning tasks.