Welcome to sktime
A unified framework for machine learning with time series
We provide specialized time series algorithms and scikit-learn compatible tools to build, tune and validate time series models for multiple learning problems, including:
- Forecasting,
- Time series classification,
- Time series regression.
For deep learning, see our companion package: sktime-dl.
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Installation
The package is available via PyPI using:
pip install sktime
Alternatively, you can install it via conda:
conda install -c conda-forge sktime
The package is actively being developed and some features may not be stable yet.
Development version
To install the development version, please see our advanced installation instructions.
Quickstart
Forecasting
from sktime.forecasting.all import *
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)
smape_loss(y_test, y_pred)
>>> 0.08661468139978168
For more, check out the forecasting tutorial.
Time Series Classification
from sktime.classification.all import *
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
X, y = load_arrow_head(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y)
classifier = TimeSeriesForest()
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
accuracy_score(y_test, y_pred)
>>> 0.8679245283018868
For more, check out the time series classification tutorial.
Documentation
- PyData Amsterdam 2020 tutorial: , [notebooks]
- Tutorial notebooks – you can run them on Binder without having to install anything!
- User guide
- API reference
How to contribute
We follow the all-contributors specification – and all kinds of contributions are welcome!
- Contributing guide
- Enhancement proposals (design discussions)
If you have a question, chat with us or raise an issue. Your help and feedback is extremely welcome!
Development roadmap
- Multivariate/panel forecasting,
- Time series clustering,
- Time series annotation (segmentation and anomaly detection),
- Probabilistic time series modelling, including survival and point processes.
Read our detailed roadmap here.
How to cite sktime
If you use sktime in a scientific publication, we would appreciate citations to the following paper:
Bibtex entry:
@inproceedings{sktime,
author = {L{\"{o}}ning, Markus and Bagnall, Anthony and Ganesh, Sajaysurya and Kazakov, Viktor and Lines, Jason and Kir{\'{a}}ly, Franz J},
booktitle = {Workshop on Systems for ML at NeurIPS 2019},
title = {{sktime: A Unified Interface for Machine Learning with Time Series}},
date = {2019},
}
