# linkedin/greykite

A flexible, intuitive and fast forecasting library

Repository: https://github.com/linkedin/greykite
Canonical: https://ross.abutalabs.com/products/greykite
Language: Python
License: BSD-2-Clause
License Family: permissive
Last push: 2025-02-20T22:01:53+00:00

## Health v2 (maintenance only)
Score: 26/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 7, release rhythm 8, longevity 100
- inputs: {"age_days": 1954, "days_push": 559, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1855, forks 107 (observed 2026-08-28T04:05:44.821968+00:00)

## What it is
Greykite is a Python time series forecasting library from LinkedIn built around its Silverkite algorithm, with support for trend, seasonality, changepoints, holidays, and autoregression. It includes an anomaly detection extension, exploratory plotting, grid search, and benchmarking tooling.

## Use cases
- forecast business metrics time series
- detect anomalies in metric monitoring
- model holiday and event effects in forecasts
- grid search forecast model parameters
- explore seasonality and changepoints in time series
- benchmark forecasting algorithms

## When to choose
- you need interpretable forecasts with trend, seasonality, changepoints, and holiday effects
- you want an end-to-end framework covering EDA, preprocessing, grid search, and evaluation
- you need tuned confidence intervals for metric anomaly alerting

## When to avoid
- you need deep-learning forecasting models like N-BEATS or Transformers as the primary approach
- you want real-time streaming anomaly detection rather than batch forecasting
- you need a maintained library with frequent releases and broad community support

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, data-science, benchmarking, data-visualization
- domain: data-science, time-series, analytics, machine-learning
- platform: python, cross-platform
- tags: time-series-forecasting, anomaly-detection, silverkite, seasonality, changepoint-detection, holiday-effects

## Member repositories
- linkedin/greykite (main) score 26

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:44.821968+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:16:44.582083+00:00, confidence not recorded.
  - readme: https://github.com/linkedin/greykite (fetched 2026-08-28T04:05:44.821968+00:00, sha 74c3bc6a8a7a)
  - registry_pypi: https://pypi.org/pypi/greykite/json (fetched 2026-08-29T10:55:50.288732+00:00, sha 4f9e13382a9c)
- Data as of 2026-08-30T08:39:29.467469+00:00.