This tutorial is about sktime - a unified framework for machine learning with time series. sktime contains algorithms and tools for building, applying, evaluating modular pipelines and composites for a variety of time series learning tasks, including forecasting, classification, regression.
sktime is easily extensible by anyone, and interoperable with the python data science stack.
This tutorial gives an up-to-date introduction to sktime base features with a focus on forecasting, model building, hierarchical/global forecasts, foundation models for forecasting, and marketplace features.
To get started, use your favorite package manager to install the required dependencies.
pip install -r requirements.txt
uv venv
uv sync
poetry install