Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-20 08:23 UTC

awslabs / gluonts

Probabilistic time series modeling in Python

Python · Jupyter NotebookApache-2.0★ 5,236 stars⑂ 837 forkssince May 2019View on GitHub ↗
KindPluginLibraryhow this is determined

awslabs/gluonts holds a health index of 94 out of 100, placing it in the Exceptional band. It scores highest on Community & Adoption (93/100) and lowest on Vitality (64/100). It was last updated 50 days ago. 4 contributors account for most of its recent work.

94
overall / 100
Exceptional

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

94
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 80 is calibrated to 94 on the published index scale (record calibration 2026-08-02).

Ownership

Amazon Web Services - LabsOrganization · verified domain
11,607 followers1,035 public repossince Jan 2013

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIgluonts0.17.0308,20211450 days agodeep-learningforecastingmachine-learningprobabilistic-forecastingtime-series

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

64Moderate · 21% of overall
How it's scored
18/36Push recency — last push 50 days ago
4.8/36Commit cadence — 7/52 weeks with commits
12.1/18Commit volume — 21 commits in the last year
10/10OpenSSF Scorecard: Maintained — 16 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year21
human_commit_share0.98
days_since_last_push50
active_weeks_last_year7
How it's scored
27/27Ships releases — 100 releases published
36/36Release recency — latest release 50 days ago
19.8/27Release cadence — a release every ~101.1 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count100
latest_release_tagv0.17.0
releases_from_tagsno
days_since_latest_release50
mean_days_between_releases101.1
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

93Exceptional · 17% of overall
How it's scored
60/60Stars — 5,236 stars
24.4/25Forks — 837 forks
10.4/15Watchers — 76 watchers
Inputs used
forks837
stars5,236
watchers76
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges4
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesshields.io
has_pull_request_templateyes
How it's scored
73.2/80Monthly downloads — 308,202 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesgluonts
dependents—
ecosystemspypi
total_downloads—
monthly_downloads308,202
unverified_packages_excluded—
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

86Excellent · 23% of overall
How it's scored
43.2/54Bus factor — 4 contributor(s) cover half of all commits
14/22.5Commit distribution — top contributor authored 38% of commits
13.5/13.5Contributor breadth — 99 contributors
10/10OpenSSF Scorecard: Contributors — project has 18 contributing companies or organizations
Inputs used
bus_factor4
contributors_sampled99
top_contributor_share0.379
How it's scored
27.4/42Issue resolution — 65% of issues closed
27.1/30PR acceptance — 1,594/1,763 decided PRs merged
0/13Newcomer PR acceptance — 0/1 first-time contributors' PRs merged in 30d
13.5/15OpenSSF Scorecard: Code-Review — Found 27/30 approved changesets -- score normalized to 9
Inputs used
merged_prs1,594
open_issues343
closed_issues643
prs_merged_7d0
prs_decided_7d1
prs_merged_30d0
prs_decided_30d1
issue_closed_ratio0.652
closed_unmerged_prs169
first_time_authors_30d1
first_time_prs_merged_30d0
first_time_prs_decided_30d1
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
25/25Owner reach — 11,607 followers of awslabs
25/25Track record — 1,035 public repos, account ~13 yr old
Inputs used
followers11,607
owner_typeOrganization
is_verifiedyes
owner_loginawslabs
public_repos1,035
account_age_days4,993

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 50 days ago
20/20Version history — 114 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesgluonts
ecosystemspypi
any_deprecatedno
min_days_since_publish50

Engineering Quality

Are baseline engineering and documentation practices in place?

83Excellent · 19% of overall
How it's scored
24/24CI workflows — 13 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff], [tool.black], [tool.isort])
0/9.6Pre-commit hooks
0/6.4.editorconfig
14/20OpenSSF Scorecard: CI-Tests — 23 out of 30 merged PRs checked by a CI test -- score normalized to 7
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://ts.gluon.ai
10/10Repository description
10/10Topics — 15 topics
0/10Wiki
Inputs used
topicstime-series, deep-learning, forecasting, neural-networks, machine-learning, time-series-prediction, time-series-forecasting, mxnet, pytorch, aws, sagemaker, timeseries, artificial-intelligence, data-science, torch
has_wikino
homepagehttps://ts.gluon.ai
docs_sitehttps://ts.gluon.ai
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

74Good · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
4.5/7.5Branch-Protection — branch protection is not maximal on development and all release branches
1.8/2.5CI-Tests — 23 out of 30 merged PRs checked by a CI test -- score normalized to 7
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
6.8/7.5Code-Review — Found 27/30 approved changesets -- score normalized to 9
2.5/2.5Contributors — project has 18 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 16 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
4.5/5SAST — SAST tool detected but not run on all commits
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 24 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate6.7
Excluded from scoring (no data or not applicable): Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages13
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:gluonts@0.17.0 runtime dependency closure — what installing the published package pulls in — 13 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

72Good · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 96 of 98 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.98
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
18/18One-command bootstrap — Justfile, docs/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff], [tool.black], [tool.isort])
11/11Static type checking — src/gluonts/core/py.typed
10/10Reproducible environment — Dockerfile
2/10Demonstrated agent practice — 1 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 2 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_filesJustfile, docs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configssrc/gluonts/core/py.typed
agent_commit_share0.01
toolchain_manifests—
dependency_bot_commit_share0.02
How it's scored
27/45Type-checkable code — Python with type-check config (src/gluonts/core/py.typed)
54.9/55Manageable file sizes — 1/864 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes159,687
source_files_sampled864
oversized_source_files1
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — examples, notebooks
Inputs used
example_dirsexamples, notebooks
has_mcp_signalno
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

5,236GitHub stars
99contributors
21commits, last 12 months
50days since last push
100releases
4bus factor
343open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 837 ⇿
0Stars
837Forks
100Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

02004006008001,000804252019-062023-012026-09
Major 0Minor 13Patch 79

Each point covers 7 days.

OpenSSF Scorecard 6.7 / 10
6.7aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-09-20 08:22 UTC

10Binary-Artifactsno binaries found in the repo
6Branch-Protectionbranch protection is not maximal on development and all release branches
7CI-Tests23 out of 30 merged PRs checked by a CI test -- score normalized to 7
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
9Code-ReviewFound 27/30 approved changesets -- score normalized to 9
10Contributorsproject has 18 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained16 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
9SASTSAST tool detected but not run on all commits
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities24 existing vulnerabilities detected
Direct dependencies 7
RegistryPackageVersion constraintManifest
PyPInumpy>=1.24,<3pyproject.toml
PyPInumpy>=1.16,<3pyproject.toml
PyPIpandas>=1.4,<3pyproject.toml
PyPIpydantic>=1.7,<3pyproject.toml
PyPItqdm~=4.23pyproject.toml
PyPItoolz~=0.10pyproject.toml
PyPItyping-extensions~=4.0pyproject.toml
All dependencies 66

Full resolved dependency set from the GitHub dependency graph: 8 direct and 58 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPInumpy—direct
PyPInumpy1.22.0direct
PyPIpandas—direct
PyPIpandas1.1.5direct
PyPIpydantic—direct
PyPItoolz—direct
PyPItqdm—direct
PyPItyping-extensions—direct
PyPIautogluon-tabular—indirect
PyPIblack—indirect
PyPIboto3—indirect
PyPIbotocore—indirect
PyPIcatch22—indirect
PyPIclick—indirect
PyPIcython—indirect
PyPIdocutils—indirect
PyPIflask—indirect
PyPIfuro2022.6.4.1indirect
PyPIgeomloss—indirect
PyPIgluonts—indirect
PyPIhierarchicalforecast—indirect
PyPIholidays—indirect
PyPIipykernel—indirect
PyPIisort—indirect
PyPIjupyter—indirect
PyPIlightgbm—indirect
PyPIlightkit—indirect
PyPIlightning—indirect
PyPIlxml—indirect
PyPImatplotlib—indirect
PyPImatplotlib3.3.2indirect
PyPImxnet—indirect
PyPImypy1.8.0indirect
PyPInbconvert—indirect
PyPInbsphinx—indirect
PyPIoptuna—indirect
PyPIprophet—indirect
PyPIpylint—indirect
PyPIpytest—indirect
PyPIpytest-cov—indirect
PyPIpytest-rerunfailures—indirect
PyPIpytest-runner—indirect
PyPIpytest-timeout—indirect
PyPIpytest-xdist—indirect
PyPIpython-dotenv—indirect
PyPIpytorch-lightning—indirect
PyPIpytorchts0.2.0indirect
PyPIrequests—indirect
PyPIrpy2—indirect
PyPIruff0.2.2indirect
PyPIs3fs—indirect
PyPIsagemaker—indirect
PyPIscikit-learn—indirect
PyPIscipy1.10.0indirect
PyPIseaborn—indirect
PyPIsetuptools—indirect
PyPIsklearn—indirect
PyPIsphinx—indirect
PyPIstatsforecast—indirect
PyPIstatsmodels—indirect
PyPItensorboard—indirect
PyPItensorboard2.3.0indirect
PyPItorch—indirect
PyPIwaitress—indirect
PyPIxgboost—indirect
PyPIxlrd—indirect
Dependency advisories 0

Installing pypi:gluonts@0.17.0 pulls in 13 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.