Skip to main content

A unified Python toolbox for machine learning with time series

Project description

A unified framework for machine learning with time series

CI

github appveyor azure codecov

Docs

readthedocs binder tutorial

Community

contributors gitter discord twitter

Code

pypi python codestyle zenodo

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.

Installation

The package is available via PyPI using:

pip install 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

How to contribute

We follow the all-contributors specification - and all kinds of contributions are welcome!

If you have a question, chat with us or raise an issue. Your help and feedback is extremely welcome!

Development roadmap

  1. Multivariate/panel forecasting,

  2. Time series clustering,

  3. Time series annotation (segmentation and anomaly detection),

  4. 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:

Markus Löning, Anthony Bagnall, Sajaysurya Ganesh, Viktor Kazakov, Jason Lines, Franz Király (2019): “sktime: A Unified Interface for Machine Learning with Time Series”

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},
}

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

sktime-0.5.0-cp38-cp38-win_amd64.whl (4.3 MB view details)

Uploaded CPython 3.8 Windows x86-64

sktime-0.5.0-cp38-cp38-manylinux2014_x86_64.whl (5.8 MB view details)

Uploaded CPython 3.8

sktime-0.5.0-cp38-cp38-manylinux2010_x86_64.whl (5.7 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ x86-64

sktime-0.5.0-cp38-cp38-macosx_10_15_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.8 macOS 10.15+ x86-64

sktime-0.5.0-cp37-cp37m-win_amd64.whl (4.3 MB view details)

Uploaded CPython 3.7m Windows x86-64

sktime-0.5.0-cp37-cp37m-manylinux2014_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.7m

sktime-0.5.0-cp37-cp37m-manylinux2010_x86_64.whl (5.5 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.12+ x86-64

sktime-0.5.0-cp37-cp37m-macosx_10_15_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.7m macOS 10.15+ x86-64

sktime-0.5.0-cp36-cp36m-win_amd64.whl (4.3 MB view details)

Uploaded CPython 3.6m Windows x86-64

sktime-0.5.0-cp36-cp36m-manylinux2014_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.6m

sktime-0.5.0-cp36-cp36m-manylinux2010_x86_64.whl (5.5 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.12+ x86-64

sktime-0.5.0-cp36-cp36m-macosx_10_15_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.6m macOS 10.15+ x86-64

File details

Details for the file sktime-0.5.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: sktime-0.5.0-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 4.3 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.0.0.post20201207 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.8.5

File hashes

Hashes for sktime-0.5.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 3052ce44603009fef36baadb991d8613493e6a807b477f5d1746c4e157fe4861
MD5 b6e23ea023b34dac547c3390bd7607b7
BLAKE2b-256 1c6efe6862e70b7c172e5f1843d13e5f65a034a614deb452e04061b4cccea379

See more details on using hashes here.

File details

Details for the file sktime-0.5.0-cp38-cp38-manylinux2014_x86_64.whl.

File metadata

  • Download URL: sktime-0.5.0-cp38-cp38-manylinux2014_x86_64.whl
  • Upload date:
  • Size: 5.8 MB
  • Tags: CPython 3.8
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/49.2.1 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.9.1

File hashes

Hashes for sktime-0.5.0-cp38-cp38-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 b8060610f1fd01b9763d69f5071b8600d41bd44ba9260d944f20d3d38a65405c
MD5 668ebff075b6a8473a1503cc3511550d
BLAKE2b-256 d1a127b6c64390fecab966c10b6969453d6b0a24841604000235ed08ba28d8a7

See more details on using hashes here.

File details

Details for the file sktime-0.5.0-cp38-cp38-manylinux2010_x86_64.whl.

File metadata

  • Download URL: sktime-0.5.0-cp38-cp38-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 5.7 MB
  • Tags: CPython 3.8, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/49.2.1 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.9.1

File hashes

Hashes for sktime-0.5.0-cp38-cp38-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 c455e914965a3109296b76981cdcbd5cdb609e1ddea612eb83aa8afb6ed0b022
MD5 603c4a1985099149031fc5cdecc4116e
BLAKE2b-256 fcaf889e77b360faa1d9f48b0349d5bc496ac374ef5b36e812bc508375a120aa

See more details on using hashes here.

File details

Details for the file sktime-0.5.0-cp38-cp38-macosx_10_15_x86_64.whl.

File metadata

  • Download URL: sktime-0.5.0-cp38-cp38-macosx_10_15_x86_64.whl
  • Upload date:
  • Size: 4.3 MB
  • Tags: CPython 3.8, macOS 10.15+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/49.2.1 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.8.6

File hashes

Hashes for sktime-0.5.0-cp38-cp38-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 e4bc637ff458a0ed20a82fdbcd8d8afc8cdbc0a258f6fc708e3a15b1395ceb3e
MD5 706a56a17e4f2ea649d750debfd8a2d7
BLAKE2b-256 7946935c92c2bc918352bb0547fb05f803f94d644ae93c5c7ccc80c702c5531f

See more details on using hashes here.

File details

Details for the file sktime-0.5.0-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: sktime-0.5.0-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 4.3 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.0.0.post20201207 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.7.9

File hashes

Hashes for sktime-0.5.0-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 f85cd2f9d15181f61f2252d6a4ba4ebc398752313607bc4d4e2ddade62f70f8b
MD5 1fde22f1e6673c85147349d9325c2964
BLAKE2b-256 689f7959830517bbea9907c88cd1781ee03a6d58a48a91be398a6384ef5ddc5a

See more details on using hashes here.

File details

Details for the file sktime-0.5.0-cp37-cp37m-manylinux2014_x86_64.whl.

File metadata

  • Download URL: sktime-0.5.0-cp37-cp37m-manylinux2014_x86_64.whl
  • Upload date:
  • Size: 5.6 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/49.2.1 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.9.1

File hashes

Hashes for sktime-0.5.0-cp37-cp37m-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 0e70e355c7b5be28aeb851094fdc15068a3cbac45ca9034ff726c116967341ee
MD5 b45c50b5790f8c2b55d0a1f16f465fef
BLAKE2b-256 05c641970a470763522651801526e7e3e6db5f58f71cf18d3480f0095755f7ba

See more details on using hashes here.

File details

Details for the file sktime-0.5.0-cp37-cp37m-manylinux2010_x86_64.whl.

File metadata

  • Download URL: sktime-0.5.0-cp37-cp37m-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 5.5 MB
  • Tags: CPython 3.7m, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/49.2.1 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.9.1

File hashes

Hashes for sktime-0.5.0-cp37-cp37m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 d8c0faeb5dc50da2cc81e7af5118e843d3e3e0f09ac8095b2e36e93cd16fd9b3
MD5 e045cfe319213bc6077efa5230e22bad
BLAKE2b-256 c8c74a96d46fef0611b00933303c2cab5fd84a51333897e6cdb978c29227e2db

See more details on using hashes here.

File details

Details for the file sktime-0.5.0-cp37-cp37m-macosx_10_15_x86_64.whl.

File metadata

  • Download URL: sktime-0.5.0-cp37-cp37m-macosx_10_15_x86_64.whl
  • Upload date:
  • Size: 4.3 MB
  • Tags: CPython 3.7m, macOS 10.15+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/47.1.0 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.7.9

File hashes

Hashes for sktime-0.5.0-cp37-cp37m-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 08ba02ac9c299337da50dd5f96acaa8642da4a37457249b26908bfe7112a897a
MD5 4e6ca9f3252d903cb5e9136c7e469f82
BLAKE2b-256 03dce16972c9c3f19d4f5f1440b00bcf96b03c10b28ea2a85dacf48105ff2c24

See more details on using hashes here.

File details

Details for the file sktime-0.5.0-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: sktime-0.5.0-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 4.3 MB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.0.0.post20201207 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.6.12

File hashes

Hashes for sktime-0.5.0-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 f950d1c1a19386bcdd0dd5ffc65f3977e4991efb6d05b8414b72e534e789496a
MD5 2624f78d8d9661756e9a189723f473d0
BLAKE2b-256 74b2072c2b3a65ff94ed95cec9143852d954ae38f3697633c2e1c400db69fcd1

See more details on using hashes here.

File details

Details for the file sktime-0.5.0-cp36-cp36m-manylinux2014_x86_64.whl.

File metadata

  • Download URL: sktime-0.5.0-cp36-cp36m-manylinux2014_x86_64.whl
  • Upload date:
  • Size: 5.6 MB
  • Tags: CPython 3.6m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/49.2.1 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.9.1

File hashes

Hashes for sktime-0.5.0-cp36-cp36m-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 66154fec136306016bda2e2b35d26f4e086b3e084e28151a018f6367ebbdd91d
MD5 51491ad8e385380d3baef39468f734c0
BLAKE2b-256 ffc287660603033afeb23ce2aa6b85d47b2dd3feb7d5074908d885c749e98900

See more details on using hashes here.

File details

Details for the file sktime-0.5.0-cp36-cp36m-manylinux2010_x86_64.whl.

File metadata

  • Download URL: sktime-0.5.0-cp36-cp36m-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 5.5 MB
  • Tags: CPython 3.6m, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/49.2.1 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.9.1

File hashes

Hashes for sktime-0.5.0-cp36-cp36m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 96acc362100c29a2b82d60c60a8a4938308e89b0e616c4da8f0180b994cc8a02
MD5 f484d0410e8ae5a1094ebc2a3faf3c69
BLAKE2b-256 6476594476619ea933e5156eb8ff4761cdf6df433e75cba05ac9f60d40a3d64c

See more details on using hashes here.

File details

Details for the file sktime-0.5.0-cp36-cp36m-macosx_10_15_x86_64.whl.

File metadata

  • Download URL: sktime-0.5.0-cp36-cp36m-macosx_10_15_x86_64.whl
  • Upload date:
  • Size: 4.3 MB
  • Tags: CPython 3.6m, macOS 10.15+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.6.12

File hashes

Hashes for sktime-0.5.0-cp36-cp36m-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 1b9a8048637a0200d9152464873966f143be26cbfb55e3a94d2df6729216f0dc
MD5 978b283c1947f44def863b1144763974
BLAKE2b-256 79c183233af37496adce926f9716abb432174b3a054f4e9a76222afabc30f187

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page