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name: sktime description: "Use sktime for time-series forecasting, classification, regression, clustering, transformations, detection, data interfaces, evaluation, benchmarking, and extension development." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

sktime

Use this repo skill when a task involves the sktime Python package: machine learning with time series through a unified scikit-learn-like estimator API. It routes forecasting, panel learning, transformations, data interfaces, detection, distances, evaluation, benchmarking, and extension-development work.

Install and import baseline

sktime 1.1.0 imports as sktime and supports Python >=3.10,<3.15. Start with the base package:

python -m pip install sktime
python -c "import sktime; print(sktime.__version__)"

Install optional extras only for the workflow being used, for example python -m pip install "sktime[forecasting]" or python -m pip install "sktime[transformations]". Avoid all_extras unless a task truly needs broad optional estimator coverage. Read references/package-overview.md for supported Python versions, dependency groups, estimator families, and package-wide constraints. Run scripts/check_env.py for an offline import and module smoke check.

Route by task

• Forecasting: use sub-skills/forecasting for ForecastingHorizon, NaiveForecaster, exogenous variables, prediction intervals, reduction, global or hierarchical forecasting, update workflows, model selection, and forecaster backtesting.
• Classification, regression, clustering: use sub-skills/panel-learning for panel estimators, load_arrow_head, TimeSeriesForestClassifier, dummy baselines, TimeSeriesKMeans, estimator tags, predict_proba, and panel train/test flows.
• Transformations and pipelines: use sub-skills/transformations-pipelines for transformers, feature extraction, differencing, imputation, lag/window features, SummaryTransformer, dunder composition, TransformedTargetForecaster, and ForecastingPipeline.
• Data containers, datasets, file I/O: use sub-skills/data-interfaces for scitype/mtype validation, check_is_mtype, convert_to, onboard versus downloaded datasets, .ts/.tsf/ARFF/UCR TSV/long formats, and tiny fixtures.
• Detection, distances, kernels, alignment: use sub-skills/detection-distances for anomaly, outlier, changepoint, segmentation, detector outputs, pairwise distances, kernels, DTW-style choices, and alignment-related APIs.
• Evaluation and benchmarking: use sub-skills/evaluation-benchmarking for temporal splitters, forecasting/detection metrics, evaluate, benchmark setup, ranking/significance analysis, and leakage checks.
• Extensions and maintenance: use sub-skills/extension-development for implementing compatible estimators, extension templates, estimator tags, soft dependency isolation, get_test_params, check_estimator, and focused contribution tests.

Common operating rules

1. Identify the sktime scitype first: Series, Panel, Hierarchical, or Table; then select the estimator scitype such as forecaster, classifier, transformer, detector, splitter, or metric.
2. Prefer a base-dependency smoke path before optional estimators. Many sktime estimators are soft-dependency wrappers; missing packages are usually solved by installing a task-specific extra or choosing a base estimator fallback.
3. Use estimator tags instead of guessing capabilities. Tags expose optional support for exogenous variables, missing values, multivariate data, probabilistic predictions, update behavior, and dependency requirements.
4. Keep temporal order intact. Forecasting evaluation and time-indexed data should use temporal splitters or backtesting, not random row shuffles.
5. Use the bundled helper scripts for offline checks. They use toy or onboard data and do not require network, model downloads, GPUs, or original notebooks.
6. For package staleness or refresh decisions, read references/repo-provenance.md. For install/import and optional-dependency failures, read references/troubleshooting.md.

Optional and unverified surfaces

Deep-learning/foundation-model adapters, CUDA acceleration, TensorFlow/torch workflows, notebook-scale examples, downloaded datasets, MLflow integrations, and broad all_extras installs are optional surfaces. This skill explains how to diagnose and route them, but it does not claim they are present in a base environment. Verify the specific extra, hardware, data access, and model cache before relying on those workflows.