Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-12 23:39 UTC

Nixtla / neuralforecast

Scalable and user friendly neural :brain: forecasting algorithms.

Python · Jupyter NotebookApache-2.0★ 4,238 stars⑂ 498 forkssince Apr 2021View on GitHub ↗
KindLibraryNetwork servicehow this is determined
#deep-learning#forecasting#esrnn#nbeats#nbeatsx#time-series#pytorch#transformer#nhits#neural-network#machine-learning#deep-neural-networks#deepar#tft#robust-regression#hierarchical-forecasting#probabilistic-forecasting#baselines#baselines-zoo#hint

Nixtla/neuralforecast holds a health index of 98 out of 100, placing it in the Exceptional band. It scores highest on Engineering Quality (96/100) and lowest on AI Readiness (55/100). It was last updated 2 days ago. 2 contributors account for most of its recent work.

98
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.

98
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 87 is calibrated to 98 on the published index scale (record calibration 2026-08-02).

Ownership

NixtlaOrganization
2,834 followers40 public repossince Mar 2021

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIneuralforecast3.2.1-448 days agotime-seriesforecastingdeep-learning

Metrics by category

Vitality

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

95Exceptional · 21% of overall

Development activity

92Excellent
How it's scored
36/36Push recency — last push 2 days ago
27.7/36Commit cadence — 40/52 weeks with commits
18/18Commit volume — 149 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year149
human_commit_share0.81
days_since_last_push2
active_weeks_last_year40

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 38 releases published
36/36Release recency — latest release 8 days ago
27/27Release cadence — a release every ~34.1 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count38
latest_release_tagv3.2.1
releases_from_tagsno
days_since_latest_release8
mean_days_between_releases34.1
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?

88Excellent · 17% of overall

Popularity & adoption

90Excellent
How it's scored
58.8/60Stars — 4,238 stars
22.5/25Forks — 498 forks
8.6/15Watchers — 36 watchers
Inputs used
forks498
stars4,238
watchers36
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

85Excellent
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
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges9
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateno

Sustainability & Governance

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

86Excellent · 23% of overall

Maintainer resilience (bus factor)

63Moderate
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
13.9/22.5Commit distribution — top contributor authored 38% of commits
13.5/13.5Contributor breadth — 57 contributors
10/10OpenSSF Scorecard: Contributors — project has 5 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled57
top_contributor_share0.381

Issue & PR responsiveness

93Exceptional
How it's scored
41.7/42Issue resolution — 99% of issues closed
25.9/30PR acceptance — 667/772 decided PRs merged
10.8/13Newcomer PR acceptance — 5/6 first-time contributors' PRs merged in 30d
15/15OpenSSF Scorecard: Code-Review — all changesets reviewed
Inputs used
merged_prs667
open_issues5
closed_issues665
prs_merged_7d3
prs_decided_7d3
prs_merged_30d19
prs_decided_30d20
issue_closed_ratio0.993
closed_unmerged_prs105
first_time_authors_30d4
first_time_prs_merged_30d5
first_time_prs_decided_30d6

Ownership & stewardship

97Exceptional
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain — verified-domain status not read for this organization
24.8/25Owner reach — 2,834 followers of Nixtla
22.6/25Track record — 40 public repos, account ~5 yr old
Inputs used
followers2,834
owner_typeOrganization
is_verified—
owner_loginNixtla
public_repos40
account_age_days1,989
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 8 days ago
20/20Version history — 44 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesneuralforecast
ecosystemspypi
any_deprecatedno
min_days_since_publish8

Engineering Quality

Are baseline engineering and documentation practices in place?

96Exceptional · 19% of overall

Engineering practices

94Exceptional
How it's scored
24/24CI workflows — 7 workflow(s)
24/24Tests present
16/16Linter config
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://nixtlaverse.nixtla.io/neuralforecast
10/10Repository description
10/10Topics — 20 topics
10/10Wiki
Inputs used
topicsdeep-learning, forecasting, esrnn, nbeats, nbeatsx, time-series, pytorch, transformer, nhits, neural-network, machine-learning, deep-neural-networks, deepar, tft, robust-regression, hierarchical-forecasting, probabilistic-forecasting, baselines, baselines-zoo, hint
has_wikiyes
homepagehttps://nixtlaverse.nixtla.io/neuralforecast
docs_sitehttps://nixtlaverse.nixtla.io/neuralforecast
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

Security posture

75Good
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
3.8/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.5/2.5CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
7.5/7.5Code-Review — all changesets reviewed
2.5/2.5Contributors — project has 5 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 — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
2/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 4
4/5SAST — SAST tool is not run on all commits -- score normalized to 8
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — no data
5.2/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
6/7.5Vulnerabilities — 2 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate7.5
Excluded from scoring (no data or not applicable): Signed-Releases. Remaining weights renormalized.

Dependency advisories

72Good
How it's scored
13.2/35Direct dependencies free of known advisories — 1 affected: pytorch-lightning 2.5.6 (high 7.8)
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
33.5/40No advisories left outstanding — 1 advisory-carrying package(s) unaddressed past 90 days; oldest published 92 days ago
Inputs used
sourceosv
advisories2
affected_packages1
assessed_packages55
unassessed_packages0
affected_by_severityhigh 1
direct_affected_packages1
Matched the pypi:neuralforecast@3.2.1 runtime dependency closure — what installing the published package pulls in — 55 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.

55Moderate · 4% of overall

Agent context & guidance

40Weak
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 78 of 81 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.963
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

73Good
How it's scored
18/18One-command bootstrap — Makefile
22/22Automated tests
11/11Lint / format config
0/11Static type checking
10/10Reproducible environment — devcontainer, lockfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
8/8Automated maintenance — 19 of the last 100 commits are automated dependency updates
4/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 4
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_filesMakefile
has_devcontaineryes
has_linter_configyes
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0.19

Code legibility for models

53Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
52.9/55Manageable file sizes — 5/134 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes132,555
source_files_sampled134
oversized_source_files5

Machine-readable interfaces

40Weak
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
0/20MCP server
40/40Runnable examples — notebooks
Inputs used
example_dirsnotebooks
has_mcp_signalno
api_schema_files—
interfaces_expected_ofnetwork-service

Key facts

4,238GitHub stars
57contributors
149commits, last 12 months
2days since last push
38releases
2bus factor
5open 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 ★ / 498 ⇿
0Stars
498Forks
38Releases

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.

0100200300400500490122021-072024-022026-08
Major 3Minor 9Patch 26

Each point covers 5 days.

OpenSSF Scorecard 7.5 / 10
7.5aggregate

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 · 2026-08-12 23:38 UTC

10Binary-Artifactsno binaries found in the repo
5Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
10Code-Reviewall changesets reviewed
10Contributorsproject has 5 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
4Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 4
8SASTSAST tool is not run on all commits -- score normalized to 8
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
7Token-Permissionsdetected GitHub workflow tokens with excessive permissions
8Vulnerabilities2 existing vulnerabilities detected
Direct dependencies 11
RegistryPackageVersion constraintManifest
PyPIcoreforecast>=0.0.6pyproject.toml
PyPIfsspec—pyproject.toml
PyPInumpy>=1.21.6pyproject.toml
PyPIpandas>=1.3.5pyproject.toml
PyPIscipy—pyproject.toml
PyPItorch>=2.9.1pyproject.toml
PyPItornado>=6.5.5pyproject.toml
PyPIpytorch-lightning>=2.0.0,<2.6.0pyproject.toml
PyPIray>=2.2.0pyproject.toml
PyPIoptuna—pyproject.toml
PyPIutilsforecast>=0.2.3pyproject.toml
All dependencies 197

Full resolved dependency set from the GitHub dependency graph: 13 direct and 184 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIcoreforecast0.0.16direct
PyPIfsspec2025.9.0direct
PyPInumpy2.2.6direct
PyPInumpy2.3.2direct
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PyPIpytorch-lightning2.5.5direct
PyPIray2.56.1direct
PyPIscipy1.15.3direct
PyPIscipy1.16.1direct
PyPItorch2.13.0direct
PyPItornado6.5.7direct
PyPIutilsforecast0.2.12direct
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PyPIfire—indirect
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PyPIpy4j0.10.9.9indirect
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PyPIpytest-cov6.2.1indirect
PyPIpython-dateutil2.9.0.post0indirect
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PyPIpytz2025.2indirect
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PyPIregex2026.4.4indirect
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PyPIrich14.1.0indirect
PyPIrpds-py0.27.1indirect
PyPIruff0.12.12indirect
PyPIs3fs2025.9.0indirect
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PyPItokenizers0.22.2indirect
PyPItomli2.2.1indirect
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PyPItqdm4.67.1indirect
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PyPItransformers5.5.0indirect
PyPItriad1.0.2indirect
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PyPItzdata2025.2indirect
PyPIurllib32.7.0indirect
PyPIvirtualenv20.36.1indirect
PyPIwcwidth0.2.13indirect
PyPIwrapt1.17.3indirect
PyPIxlstm2.0.0indirect
PyPIxlstm2.0.5indirect
PyPIyarl1.20.1indirect
Dependency advisories 1

Installing pypi:neuralforecast@3.2.1 pulls in 55 packages, direct and transitive: 1 carry known advisories, of which 1 are direct dependencies.

PackageVersionRelationSeverityAdvisoriesFixed in
pytorch-lightning2.5.6directhigh2—

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

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Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.31.0 — full methodology · metrics wiki.

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