PyRank
Nixtla

mlforecast

Scalable machine 🤖 learning for time series forecasting.
PyPI GitHub
More links
Download Stats
Monthly downloads
328.3K
Weekly downloads
84.7K
GitHub stars
1.3K

Downloads in the 30 and 7 days to October 10, 2026. Refreshed weekly.

Package Information

Latest Version
1.1.0
Requires Python
>=3.10
Licence
Apache Software License 2.0
Last Release
3 months ago
First Release
April 27, 2021
Total Releases
58
Python Versions
3.10 3.11 3.12 3.13 3.14
No reported malware or high/critical advisories · Details

The current version has no OSV.dev malware reports and no critical or high-severity vulnerabilities.

Package Insights

What's this?
Score breakdown - each component scores 0-100, weighted by percentage
Maintenance
100
Popularity
65
Adoption
85
Community
60
Provenance
0
How the maintenance status was determined
Last commit
6 days ago
Monthly downloads
328,258
• 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
328,258
Stars
1,282
Stars per 1k downloads
3.91
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
12,097
Month daily avg
10,942
Momentum
+10.6%
((week_daily_avg - month_daily_avg) / month_daily_avg) * 100
More insights and metrics
Python version support tiers
requires_python
>=3.10
Cutting Edge
Minimum supported version is 3.14 or newer
Modern
Minimum supported version is 3.11 to 3.13
• Legacy
Supports 3.10 or older (or Python 2)
Unknown
No requires_python specified or unparseable
License categories
License
Apache Software License 2.0
• 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
98 days ago
Total releases
58
Releases/year
10.6
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
33
Monthly downloads
328,258
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
76
 
Issue Burden
11.7
per 1k stars
Discussions
60.1
per 1k stars
Fork Ratio
23.6%
contrib / forks
Stale Release
91d
 
PR Velocity
Active
 
Not all downloads include detailed metadata. Breakdowns reflect the subset that does.

Downloads by OS

Linux 4.9K
Windows 107
macOS 98

Python Versions

3.12
34.1% 1.6K
3.11
31.7% 1.5K
3.13
17.9% 851
3.10
10.5% 502
3.14
5.8% 276

Top Countries

1. US US
3.6K
2. NL NL
555
3. IE IE
296
4. DE DE
247
5. SE SE
88
6. SG SG
78
7. BE BE
62
8. FI FI
46
9. KR KR
41
10. GB GB
40

Weekly Downloads

GitHub Stars

Dependencies

8
16 optional dependencies
  • dask[dask]
    extra == "dask"
  • fsspec[gcp]
    extra == "gcp"
  • fsspec[azure]
    extra == "azure"
  • fsspec[aws]
    extra == "aws"
  • fugue>=0.9.4[dask]
    extra == "dask"
  • fugue>=0.9.4[spark]
    extra == "spark"
  • fugue>=0.9.4[ray]
    (sys_platform != "win32" and python_version < "3.14") and extra == "ray"
  • lightgbm[spark]
    extra == "spark"
  • lightgbm[dask]
    extra == "dask"
  • lightgbm-ray[ray]
    (sys_platform != "win32" and python_version < "3.14") and extra == "ray"
  • polars[polars]
    extra == "polars"
  • pyspark>=3.3[spark]
    extra == "spark"
  • xgboost[dask]
    extra == "dask"
  • xgboost<2.1.0,>=2.0.0[ray]
    (sys_platform != "win32" and python_version < "3.14") and extra == "ray"
  • xgboost[spark]
    extra == "spark"
  • xgboost-ray>=0.1.19[ray]
    (sys_platform != "win32" and python_version < "3.14") and extra == "ray"

Used By

7

Repository details

Forks
140
Issues
15
Last Commit
1 week ago
Contributors
33
Open PRs
17
Archived
No

Data and sources

Tracking Since
September 26, 2026
Data Updated
October 11, 2026
Data Freshness
Fresh
Keywords
python forecast forecasting machine-learning dask
Sources and methodology