No known security advisories.
The current version has no OSV.dev malware reports and no critical or high-severity vulnerabilities.
sktime has 1.2M monthly downloads and 10K GitHub stars. Version 1.0.1 supports Python <3.15,>=3.10, first published in 2019.
It is actively maintained under a permissive license, last released in Jun 2026.
It is built on top of joblib, numpy and packaging. It is used by 76 tracked packages, including pycaret, evalml and lightwood.
GitHub topics include time-series, machine-learning and scikit-learn.
Package Insights
Score breakdown - each component scores 0-100, weighted by percentage
Maintenance
100
Popularity
85
Adoption
80
Community
90
Provenance
100
How the maintenance status was determined
Last commit
2 days ago
Monthly downloads
1,189,360
•
Active
Activity within 30 days
Stable
Activity within 31-180 days
Minimal
Activity within 181-365 days, or no activity data
At Risk
No activity for > 365 days, but > 10,000 monthly downloads
Archived
Repository flagged as archived on GitHub
How the popularity category was determined
Monthly downloads
1,189,360
Stars
9,832
Stars per 1k downloads
8.27
Industry Standard
> 10,000,000 monthly downloads
Hidden Gem
< 0.5 stars per 1k downloads and > 100,000 monthly downloads
•
Community Favorite
> 5 stars per 1k downloads
Rising Star
Balanced ratio of stars to downloads
Unknown
Fewer than 1,000 monthly downloads, or no star data
How download momentum is calculated
Week daily avg
39,844
Month daily avg
39,645
Momentum
+0.5%
((week_daily_avg - month_daily_avg) / month_daily_avg) * 100Python version support tiers
requires_python
<3.15,>=3.10
Cutting Edge
Minimum supported version is 3.12 or newer
•
Modern
Minimum supported version is 3.9, 3.10, or 3.11
Legacy
Supports 3.8 or older (or Python 2)
Unknown
No requires_python specified or unparseable
License categories
License
BSD 3-Clause License
•
Permissive
MIT, Apache, BSD, ISC, Unlicense, CC0, etc.
Copyleft
GPL, LGPL, AGPL, MPL, EPL, etc.
Proprietary
Commercial, all rights reserved, custom EULAs
Unknown
No license specified, or none of the above match
How the release cadence was determined
Last release
25 days ago
Total releases
101
Releases/year
13.8
•
Rapid
Last release within 365 days and > 12 releases/year
Regular
Last release within 365 days
Slow
Last release 366-730 days ago
Stalled
Last release > 730 days ago, or no releases
Unknown
No usable release date available
How contributor risk was determined
Contributors
575
Monthly downloads
1,189,360
High Load
≤ 2 contributors and ≥ 50,000 monthly downloads
Moderate Load
< 10 contributors and ≥ 500,000 monthly downloads
Low Load
< 25 contributors and ≥ 5,000,000 monthly downloads, or sole maintainer with ≥ 10,000 monthly downloads
•
Balanced
Enough contributors for the package's popularity
Unknown
No contributor data available
Engagement
97
Issue Burden
143.4
per 1k stars
Discussions
42.1
per 1k stars
Fork Ratio
26.1%
contrib / forks
Stale Release
No
PR Velocity
Active
Weekly Downloads
GitHub Stars
Not all downloads include detailed metadata. Breakdowns reflect the subset that does.
Downloads by OS
Linux
22.5K
macOS
238
Windows
159
Python Versions
3.10
38.8%
8.5K
3.12
37.3%
8.2K
3.11
17.9%
3.9K
3.13
3.2%
692
3.9
2.9%
630
Top Countries
1.
US
13.1K
US
2.
IE
3.5K
IE
3.
IN
2.9K
IN
4.
NL
988
NL
5.
SG
823
SG
6.
JP
595
JP
7.
BR
589
BR
8.
HK
177
HK
9.
GB
163
GB
10.
DE
121
DE
Dependencies
7
- joblib <1.6,>=1.2.0
- numpy <2.5,>=1.21
- packaging
- pandas <3.0.0,>=1.1
- scikit-base <1.1.0,>=1.0.0
- scikit-learn <1.8.0,>=0.24
- scipy <2.0.0,>=1.2
109 optional dependencies
- accelerate[dl]
- arch[forecasting]
- backoff[dev]
- boto3[mlflow-tests]
- botocore[mlflow-tests]
- catboost[compatibility-tests]
- cloudpickle[all-extras]
- dtaidistance[alignment]
- dtw-python[alignment]
- dtw-python[notebooks]
- einops[dl]
- gluonts[dl]
- hmmlearn[detection]
- hmmlearn[annotation]
- holidays[all-extras]
- holidays[transformations]
- httpx[dev]
- huggingface-hub[datasets]
- huggingface-hub[dl]
- hydra-core[dl]
- jupyter[docs]
- jupyter[binder]
- lightning[dl]
- lightning[notebooks]
- matplotlib[all-extras]
- matplotlib[notebooks]
- mlflow[mlflow]
- mlflow[mlflow2]
- mlflow[mlflow-tests]
- moto[mlflow-tests]
- mrseql[cython-extras]
- mrsqm[cython-extras]
- myst-parser[docs]
- nbsphinx[docs]
- networkx[clustering]
- neuralforecast[dl]
- numba[detection]
- numba[regression]
- numba[transformations]
- numba[alignment]
- numba[all-extras]
- numba[annotation]
- numba[classification]
- numba[clustering]
- numba[cython-extras]
- numpy[notebooks]
- numpy[numpy1]
- numpydoc[docs]
- pandas[pandas1]
- peft[dl]
- pmdarima[notebooks]
- pmdarima[forecasting]
- polars[all-extras]
- pre-commit[dev]
- prophet[forecasting]
- prophet[notebooks]
- pycatch22[transformations]
- pydata-sphinx-theme[docs]
- pyod[detection]
- pyod[annotation]
- pytest[tests]
- pytest[dev]
- pytest-randomly[tests]
- pytest-randomly[dev]
- pytest-timeout[tests]
- pytest-timeout[dev]
- pytest-xdist[tests]
- pytest-xdist[dev]
- pytorch-forecasting[dl]
- pytorch-forecasting[notebooks]
- rdata[datasets]
- requests[datasets]
- scipy[notebooks]
- seaborn[all-extras]
- seaborn[notebooks]
- seasonal[param-est]
- simdkalman[transformations]
- skchange[binder]
- skforecast[forecasting]
- skpro[all-extras]
- skpro[forecasting]
- skpro[notebooks]
- sphinx[docs]
- sphinx-copybutton[docs]
- sphinx-design[docs]
- sphinx-gallery[docs]
- sphinx-issues[docs]
- statsforecast[forecasting]
- statsforecast[notebooks]
- statsmodels[all-extras]
- statsmodels[forecasting]
- statsmodels[param-est]
- statsmodels[transformations]
- tabulate[docs]
- tbats[notebooks]
- tensorflow[classification]
- tensorflow[dl]
- tensorflow[networks]
- tensorflow[regression]
- torch[dl]
- torch[notebooks]
- torchmetrics[dl]
- tqdm[dl]
- transformers[dl]
- ts2vg[clustering]
- tsfresh[classification]
- tsfresh[transformations]
- tslearn[clustering]
- wheel[dev]
Used By
76
- pycaret
- evalml
- lightwood
- skchange
- torch-timeseries
- dfm-python
- fedot
- ml-trainer-sdk
- prophetverse
- automar
- tsinterpret
- sits
- atom-ml
- dtaianomaly
- scitex-ml
- nanosense
- fcst
- diive
- imputegap
- cbr-fox
- uea-ucr-datasets
- timeseriesfeatures
- optrade
- xai4tsc
- torchtime
- sktime-mcp
- torchchronos
- fedot-ind
- quantminer
- pyspi
- mlops-batch-prediction-pipeline
- experionml
- mlops-training-pipeline
- macroframe-forecast
- gloria
- tsagentkit
- forecaster-toolkit
- cat-spend-training-pipeline
- forecast-combine
- energy-consumption-forecasting
- hybridts
- evalml-automining
- g-batch-prediction-pipeline
- eeg-emotion-recognition
- tsfuse
- mf-excel
- blocktorch
- g-training-pipeline
- batch-prediction-pipeline-amar
- batch-prediction-pipeline-mm
GitHub Topics
Tags from the package's GitHub repository describing what it does.
Package Information
License
BSD 3-Clause License
First Release
March 21, 2019
Last Release
3 weeks ago
Last Commit
3 days ago
Latest Version
1.0.1
Total Releases
101
Contributors
575
Open PRs
972
Archived
No
Python Versions
3.10
3.11
3.12
3.13
3.14
Wheels
cp36
cp37
cp38
cp39
py3
Keywords
Tracking Since
January 6, 2026
Data Updated
July 5, 2026
Data Freshness
Fresh