A
licenseB
qualityD
maintenanceEnable any AI agent to forecast time-series data (e.g., sales, traffic) using Google's TimesFM or a zero-dependency statistical baseline.
3
Apache 2.0
skforecast-ai
Officialby skforecast
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- AlicenseNot gradedqualityCmaintenanceEnables zero-shot time-series forecasting locally on CPU by turning numeric series into median and quantile forecasts. It exposes forecast and health tools, with the local forecasting service started on demand.Apache 2.0
- AlicenseNot gradedqualityCmaintenanceLocal MCP server for GPU-backed TimesFM 2.5 forecasting, enabling zero-shot time-series forecasting, covariate forecasting, anomaly detection, and CSV forecasting via MCP tools.2Apache 2.0
- AlicenseAqualityBmaintenanceEnables users to perform deterministic time series forecasting through Claude by running reproducible Python statistical and foundation models, providing tools for loading data, cross-validation, forecasting, anomaly detection, and exporting re-runnable manifests without any LLM involvement.8MIT
- FlicenseAqualityCmaintenanceEnables demand forecasting and replenishment recommendations using statistical models (Syntetos-Boylan classification, AutoETS, TSB) and provides tools for forecasting, evaluation, and order quantity calculation.6-
- AlicenseAqualityAmaintenanceEnables AI clients to produce probabilistic forecasts with prediction intervals for numeric time series such as sales, demand, traffic, and sensor readings, using a panel of zero-shot foundation models or an accuracy-weighted ensemble. Supports up to 64 series per call, optional covariates, horizons up to 512 steps, and usage or credit checks, all without training or feature engineering.4118 npmMIT
TDQS
A3.6/5.0
Scored across 11 tools
Disambiguation4/5
Most tools have clearly distinct purposes: profile builds a profile, plan builds a plan, create_cv builds a CV strategy, and forecast/backtest/compare each run in distinct modes. The main risk is soft overlap between forecast, backtest, and compare (all execute a plan), but their outputs and semantics differ enough to distinguish them.
Naming Consistency4/5
Names are readable and mostly follow a verb_noun pattern (refine_plan, create_cv, get_code, get_failure, list_objects, describe_object), but several are bare verbs (forecast, backtest, compare, profile, plan), which is a minor deviation. No chaotic mixing of case or conventions.
Tool Count5/5
11 tools is well-scoped for a forecasting workflow server. Each tool maps to a distinct stage (profile, plan, CV, run, compare, inspect) and none feels redundant.
Completeness4/5
The lifecycle is largely covered: profile -> plan -> create_cv -> forecast/backtest/compare, plus inspection via list_objects, describe_object, get_code, and get_failure, and refinement via refine_plan. Minor gaps exist (no explicit cancel or delete tool despite mentions of cancellation and object storage), but agents can work around these.
Maintenance
ActivityMaintained
ResponsivenessSyncing