io.github.winedarksea/AutoTS

MCPcommunity
v1.0.1io.github.winedarkseaUnknownUpdated 5mo agoGitHub

Automated time series forecasting with model search, anomaly detection, and event risk analysis

AutoTS is a time series package for Python designed for rapidly deploying high-accuracy forecasts at scale. Give it a try in your browser with the official demo app. In 2023, AutoTS won in the M6 forecasting competition, delivering the highest performance investment decisions across 12 months of stock market forecasting. There are dozens of forecasting models usable in the style of and . These…

Automatically indexed from public sources. Not yet verified by the developer on Forge.Claim this listing →
5mo agoLast update
Package
Authorio.github.winedarksea
LicenseUnknown
Version1.0.1
Sourcemcp-registry
Trust Status
B
60/100Good
Listed in Forge index+10/10
Publisher identity verified+0/25
Publisher: run `forge publish` from the package repo to claim ownership
Ed25519 publish signature+0/10
Included automatically when the publisher runs `forge publish`
Domain verification+0/5
Publisher: host /.well-known/forge.json on the package homepage with { "publisher": "<github-login>" }
CVE scan · clean+30/30
Static analysis · clean+20/20
npm provenance (Sigstore)+0/5
Publish from GitHub Actions with the --provenance flag
Paste into Claude Code, Cursor, or any AI assistant to fix all gaps
StatusCommunity-indexed
PublisherUnverified
SignatureUnsigned
Domain
Provenance
DependenciesNot audited
Tool surface
Security scan✓ Cleanv1.0.2 · 19d ago
EvalsNone
IndexedJun 13, 2026

Verification confirms publisher identity (repo ownership), not code safety. The security scan covers known CVEs and suspicious install scripts — it cannot prove the absence of malicious code.

About

AutoTS is a time series package for Python designed for rapidly deploying high-accuracy forecasts at scale. Give it a try in your browser with the official demo app. In 2023, AutoTS won in the M6 forecasting competition, delivering the highest performance investment decisions across 12 months of stock market forecasting. There are dozens of forecasting models usable in the style of and . These includes naive, statistical, machine learning, and deep learning models. Additionally, there are over…

Keywords
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