Welcome to sktime
A unified interface for machine learning with time series
🚀 Version 0.24.1 out now! Check out the release notes here.
sktime is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks. Currently, this includes time series classification, regression, clustering, annotation, and forecasting. It comes with time series algorithms and scikit-learn compatible tools to build, tune and validate time series models.
|⭐ Tutorials||New to sktime? Here's everything you need to know!|
|📋 Binder Notebooks||Example notebooks to play with in your browser.|
|👩💻 User Guides||How to use sktime and its features.|
|✂️ Extension Templates||How to build your own estimator using sktime's API.|
|🎛️ API Reference||The detailed reference for sktime's API.|
|📺 Video Tutorial||Our video tutorial from 2021 PyData Global.|
|🛠️ Changelog||Changes and version history.|
|🌳 Roadmap||sktime's software and community development plan.|
|📝 Related Software||A list of related software.|
💬 Where to ask questions
Questions and feedback are extremely welcome! We strongly believe in the value of sharing help publicly, as it allows a wider audience to benefit from it.
|🐛 Bug Reports||GitHub Issue Tracker|
|✨ Feature Requests & Ideas||GitHub Issue Tracker|
|👩💻 Usage Questions||GitHub Discussions · Stack Overflow|
|💬 General Discussion||GitHub Discussions|
|🏭 Contribution & Development||
|🌐 Community collaboration session||Discord - Fridays 3 pm UTC, dev/meet-ups channel|
Our objective is to enhance the interoperability and usability of the time series analysis ecosystem in its entirety. sktime provides a unified interface for distinct but related time series learning tasks. It features dedicated time series algorithms and tools for composite model building such as pipelining, ensembling, tuning, and reduction, empowering users to apply an algorithm designed for one task to another.
For deep learning, see our companion package: sktime-dl.
|Forecasting||stable||Tutorial · API Reference · Extension Template|
|Time Series Classification||stable||Tutorial · API Reference · Extension Template|
|Time Series Regression||stable||API Reference|
|Transformations||stable||Tutorial · API Reference · Extension Template|
|Parameter fitting||maturing||API Reference · Extension Template|
|Time Series Clustering||maturing||API Reference · Extension Template|
|Time Series Distances/Kernels||maturing||Tutorial · API Reference · Extension Template|
|Time Series Alignment||experimental||API Reference · Extension Template|
|Distributions and simulation||experimental|
⏳ Install sktime
For troubleshooting and detailed installation instructions, see the documentation.
- Operating system: macOS X · Linux · Windows 8.1 or higher
- Python version: Python 3.8, 3.9, 3.10, 3.11, and 3.12 (only 64-bit)
- Package managers: pip · conda (via
Using pip, sktime releases are available as source packages and binary wheels. Available wheels are listed here.
pip install sktime
or, with maximum dependencies,
pip install sktime[all_extras]
For curated sets of soft dependencies for specific learning tasks:
pip install sktime[forecasting] # for selected forecasting dependencies pip install sktime[forecasting,transformations] # forecasters and transformers
or similar. Valid sets are:
Cave: in general, not all soft dependencies for a learning task are installed, only a curated selection.
You can also install sktime from
conda via the
The feedstock including the build recipe and configuration is maintained
in this conda-forge repository.
conda install -c conda-forge sktime
or, with maximum dependencies,
conda install -c conda-forge sktime-all-extras
conda does not support dependency sets,
flexible choice of soft dependencies is unavailable via
from sktime.datasets import load_airline from sktime.forecasting.base import ForecastingHorizon from sktime.forecasting.model_selection import temporal_train_test_split from sktime.forecasting.theta import ThetaForecaster from sktime.performance_metrics.forecasting import mean_absolute_percentage_error y = load_airline() y_train, y_test = temporal_train_test_split(y) fh = ForecastingHorizon(y_test.index, is_relative=False) forecaster = ThetaForecaster(sp=12) # monthly seasonal periodicity forecaster.fit(y_train) y_pred = forecaster.predict(fh) mean_absolute_percentage_error(y_test, y_pred) >>> 0.08661467738190656
Time Series Classification
from sktime.classification.interval_based import TimeSeriesForestClassifier from sktime.datasets import load_arrow_head from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score X, y = load_arrow_head() X_train, X_test, y_train, y_test = train_test_split(X, y) classifier = TimeSeriesForestClassifier() classifier.fit(X_train, y_train) y_pred = classifier.predict(X_test) accuracy_score(y_test, y_pred) >>> 0.8679245283018868
👋 How to get involved
There are many ways to join the sktime community. We follow the all-contributors specification: all kinds of contributions are welcome - not just code.
|💝 Contribute||How to contribute to sktime.|
|🎒 Mentoring||New to open source? Apply to our mentoring program!|
|📅 Meetings||Join our discussions, tutorials, workshops, and sprints!|
|👩🔧 Developer Guides||How to further develop sktime's code base.|
|🚧 Enhancement Proposals||Design a new feature for sktime.|
|🏅 Contributors||A list of all contributors.|
|🙋 Roles||An overview of our core community roles.|
|💸 Donate||Fund sktime maintenance and development.|
|🏛️ Governance||How and by whom decisions are made in sktime's community.|
💡 Project vision
- By the community, for the community -- developed by a friendly and collaborative community.
- The right tool for the right task -- helping users to diagnose their learning problem and suitable scientific model types.
- Embedded in state-of-art ecosystems and provider of interoperable interfaces -- interoperable with scikit-learn, statsmodels, tsfresh, and other community favorites.
- Rich model composition and reduction functionality -- build tuning and feature extraction pipelines, solve forecasting tasks with scikit-learn regressors.
- Clean, descriptive specification syntax -- based on modern object-oriented design principles for data science.
- Fair model assessment and benchmarking -- build your models, inspect your models, check your models, and avoid pitfalls.
- Easily extensible -- easy extension templates to add your own algorithms compatible with sktime's API.