Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-19 19:53 UTC

Nixtla / datasetsforecast

Datasets for time series forecasting

Python · Jupyter NotebookMIT★ 128 stars⑂ 12 forkssince Jun 2022View on GitHub ↗

Nixtla/datasetsforecast holds a health index of 93 out of 100, placing it in the Exceptional band. It scores highest on Engineering Quality (96/100) and lowest on Community & Adoption (68/100). It was last updated today. A single contributor accounts for most of its recent work.

93
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.

93
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 79 is calibrated to 93 on the published index scale (record calibration 2026-08-02).

Ownership

NixtlaOrganization · verified domain
2,866 followers42 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
PyPIdatasetsforecast1.0.254,211113 days agotime-seriesforecastingdatasets

Metrics by category

Vitality

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

77Good · 21% of overall
How it's scored
36/36Push recency — last push 0 days ago
12.5/36Commit cadence — 18/52 weeks with commits
14.1/18Commit volume — 36 commits in the last year
10/10OpenSSF Scorecard: Maintained — 15 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year36
human_commit_share0.7
days_since_last_push0
active_weeks_last_year18
How it's scored
27/27Ships releases — 10 releases published
36/36Release recency — latest release 3 days ago
12.6/27Release cadence — a release every ~175.2 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count10
latest_release_tagv1.0.2
releases_from_tagsno
days_since_latest_release3
mean_days_between_releases175.2
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?

68Good · 17% of overall
How it's scored
34.1/60Stars — 128 stars
8.7/25Forks — 12 forks
4.3/15Watchers — 7 watchers
Inputs used
forks12
stars128
watchers7
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 (MIT)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_services—
has_pull_request_templateno
How it's scored
63.1/80Monthly downloads — 54,211 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesdatasetsforecast
dependents—
ecosystemspypi
total_downloads—
monthly_downloads54,211
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?

78Good · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
9.2/22.5Commit distribution — top contributor authored 59% of commits
13.5/13.5Contributor breadth — 13 contributors
10/10OpenSSF Scorecard: Contributors — project has 4 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled13
top_contributor_share0.591
How it's scored
30.8/42Issue resolution — 73% of issues closed
25.7/30PR acceptance — 84/98 decided PRs merged
13/13Newcomer PR acceptance — 5/5 first-time contributors' PRs merged in 30d
15/15OpenSSF Scorecard: Code-Review — all changesets reviewed
Inputs used
merged_prs84
open_issues4
closed_issues11
prs_merged_7d5
prs_decided_7d5
prs_merged_30d6
prs_decided_30d6
issue_closed_ratio0.733
closed_unmerged_prs14
first_time_authors_30d2
first_time_prs_merged_30d5
first_time_prs_decided_30d5
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
24.9/25Owner reach — 2,866 followers of Nixtla
23.2/25Track record — 42 public repos, account ~5 yr old
Inputs used
followers2,866
owner_typeOrganization
is_verifiedyes
owner_loginNixtla
public_repos42
account_age_days2,056

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 3 days ago
20/20Version history — 11 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesdatasetsforecast
ecosystemspypi
any_deprecatedno
min_days_since_publish3

Engineering Quality

Are baseline engineering and documentation practices in place?

96Exceptional · 19% of overall
How it's scored
24/24CI workflows — 6 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/datasetsforecast
10/10Repository description
10/10Topics — 2 topics
10/10Wiki
Inputs used
topicsdatasets, forecasting
has_wikiyes
homepagehttps://nixtlaverse.nixtla.io/datasetsforecast
docs_sitehttps://nixtlaverse.nixtla.io/datasetsforecast
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

75Good · 16% of overall
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 4 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 — 15 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — no data
2/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 4
5/5SAST — SAST tool is run on all commits
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
5.2/7.5Vulnerabilities — 3 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate6.9
Excluded from scoring (no data or not applicable): Packaging, 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_packages37
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:datasetsforecast@1.0.2 runtime dependency closure — what installing the published package pulls in — 37 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.

69Good · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://nixtlaverse.nixtla.io/llms.txt)
35.8/40Legible commit history — 47 of 70 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://nixtlaverse.nixtla.io/llms.txt
legible_history_share0.671
agent_instruction_files—
agent_instruction_max_bytes—
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
4/10Demonstrated agent practice — 2 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 30 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_share0.02
toolchain_manifests—
dependency_bot_commit_share0.3
How it's scored
0/45Type-checkable code — Python without a type-check config
55/55Manageable file sizes — 0/25 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes43,523
source_files_sampled25
oversized_source_files0
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 — notebooks
Inputs used
example_dirsnotebooks
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

128GitHub stars
13contributors
36commits, last 12 months
0days since last push
10releases
1bus factor
4open 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 ★ / 12 ⇿
0Stars
12Forks
2Releases

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.

0246810121212022-102024-062026-01
Major 1Minor 0Patch 1

Each point covers 3 days.

OpenSSF Scorecard 6.9 / 10
6.9aggregate

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 · 2026-10-19 19:53 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 4 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained15 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
4Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 4
10SASTSAST tool is run on all commits
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
7Vulnerabilities3 existing vulnerabilities detected
Direct dependencies 9
RegistryPackageVersion constraintManifest
PyPIaiohttp>=3.14.1pyproject.toml
PyPInumpy—pyproject.toml
PyPIscikit-learn—pyproject.toml
PyPIpandas<3.0.0pyproject.toml
PyPIpyarrow>=15.0.0pyproject.toml
PyPIrequests—pyproject.toml
PyPItqdm—pyproject.toml
PyPIutilsforecast>=0.0.8pyproject.toml
PyPIxlrd>=1.0.0pyproject.toml
All dependencies 77

Full resolved dependency set from the GitHub dependency graph: 11 direct and 66 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIaiohttp3.14.3direct
PyPInumpy2.2.6direct
PyPInumpy2.4.1direct
PyPIpandas2.3.3direct
PyPIpyarrow25.0.1direct
PyPIrequests2.33.0direct
PyPIscikit-learn1.7.2direct
PyPIscikit-learn1.8.0direct
PyPItqdm4.67.1direct
PyPIutilsforecast0.2.15direct
PyPIxlrd2.0.2direct
PyPIaiohappyeyeballs2.6.1indirect
PyPIaiosignal1.4.0indirect
PyPIasync-timeout5.0.1indirect
PyPIattrs25.4.0indirect
PyPIbackports-asyncio-runner1.2.0indirect
PyPIcertifi2026.1.4indirect
PyPIcfgv3.5.0indirect
PyPIcharset-normalizer3.4.4indirect
PyPIcolorama0.4.6indirect
PyPIcontourpy1.3.2indirect
PyPIcontourpy1.3.3indirect
PyPIcoverage7.13.2indirect
PyPIcycler0.12.1indirect
PyPIdatasetsforecast1.0.1indirect
PyPIdistlib0.4.0indirect
PyPIexceptiongroup1.3.1indirect
PyPIfilelock3.32.7indirect
PyPIfonttools4.61.1indirect
PyPIfrozenlist1.8.0indirect
PyPIgriffe1.15.0indirect
PyPIgriffe2md1.2.6indirect
PyPIidentify2.6.16indirect
PyPIidna3.15indirect
PyPIiniconfig2.3.0indirect
PyPIjinja23.1.6indirect
PyPIjoblib1.5.3indirect
PyPIkiwisolver1.4.9indirect
PyPImarkdown-it-py4.0.0indirect
PyPImarkupsafe3.0.3indirect
PyPImatplotlib3.10.8indirect
PyPImdformat1.0.0indirect
PyPImdurl0.1.2indirect
PyPImkdocstrings-parser0.0.1indirect
PyPImultidict6.9.1indirect
PyPInarwhals2.15.0indirect
PyPInodeenv1.10.0indirect
PyPIpackaging26.0indirect
PyPIpillow12.3.0indirect
PyPIpip-licenses5.5.1indirect
PyPIplatformdirs4.5.1indirect
PyPIpluggy1.6.0indirect
PyPIpre-commit4.5.1indirect
PyPIprettytable3.17.0indirect
PyPIpropcache0.4.1indirect
PyPIpygments2.20.0indirect
PyPIpyparsing3.3.2indirect
PyPIpytest9.0.3indirect
PyPIpytest-asyncio1.3.0indirect
PyPIpytest-cov7.0.0indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpython-discovery1.6.2indirect
PyPIpytz2025.2indirect
PyPIpyyaml6.0.3indirect
PyPIrich14.3.1indirect
PyPIscipy1.15.3indirect
PyPIscipy1.17.0indirect
PyPIsix1.17.0indirect
PyPIthreadpoolctl3.6.0indirect
PyPItomli2.4.0indirect
PyPItypes-requests2.32.4.20260107indirect
PyPItyping-extensions4.15.0indirect
PyPItzdata2025.3indirect
PyPIurllib32.8.0indirect
PyPIvirtualenv21.7.13indirect
PyPIwcwidth0.5.0indirect
PyPIyarl1.22.0indirect
Dependency advisories 0

Installing pypi:datasetsforecast@1.0.2 pulls in 37 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.