A unified Python toolbox for machine learning with time series
Project description
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 sktimeAlternatively, you can install it via conda:
conda install -c conda-forge sktimeThe 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.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 smape_loss
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)
>>> 0.08661468139978168For more, check out the forecasting tutorial.
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(return_X_y=True)
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)
>>> 0.8679245283018868For more, check out the time series classification tutorial.
Documentation
PyData Amsterdam 2020 tutorial: [video], [notebooks]
Tutorial notebooks - you can run them on Binder without having to install anything!
How to contribute
We follow the all-contributors specification - and all kinds of contributions are welcome!
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},
}Release history Release notifications | RSS feed
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