The project is named Greykite (package name greykite), not GreyKite or GrayKite. It is LinkedIn’s open-source Python framework for business and operational time-series forecasting, centered on the interpretable Silverkite algorithm. The latest release listed on PyPI as of August 18, 2026 is 1.1.0, released February 20, 2025. Its PyPI metadata declares Python 3.10 or newer and lists Python 3.10–3.12.
Greykite is more than one estimator: it combines data preparation, feature engineering, model fitting, template-based configuration, grid search, backtesting, plotting, prediction intervals, and anomaly-detection functionality. It is a strong candidate when calendar effects, changing trends, events, and interpretability matter.
What is Greykite?
Greykite is an open-source forecasting framework created by LinkedIn and distributed under the BSD 2-Clause License. The installable package is greykite. PyPI describes it as a toolkit for time-series forecasting with Silverkite, Prophet integration, Auto-ARIMA-related functionality, preprocessing, evaluation, benchmarking, and visualization.
The framework and algorithm are different things:
- Greykite is the end-to-end Python framework.
- Silverkite is its flagship feature-engineered, regression-based forecasting algorithm.
- Greykite AD extends the project toward operational anomaly detection and alert-threshold tuning.
The documentation site still labels version 1.0.0 as its latest documentation release, while PyPI lists 1.1.0 as the newest package release. Check the installed version’s API and output schema rather than assuming documentation examples are unchanged.
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Sources: PyPI Greykite 1.1.0, Greykite documentation index, GitHub releases.
What Silverkite does
Silverkite turns a timestamped series into explanatory features and fits a forecasting model that can represent:
- Trend and multiple seasonalities.
- Automatically detected changepoints.
- Public holidays and custom events.
- Autoregressive and lag-based effects.
- Known external regressors such as promotions, prices, weather forecasts, or maintenance schedules.
- Prediction intervals and component-level diagnostics.
This is a flexible, interpretable forecasting approach, not a generic deep-learning model. Feature contributions, model summaries, and component plots can help explain why a forecast changes, although they do not establish causal relationships.
Sources: Silverkite overview and prediction-band documentation.
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Data Greykite expects
A typical input is a regularly sampled dataframe containing one timestamp column and one target column. Daily, hourly, weekly, and other business frequencies can be used when the data and configuration support them. Holiday calendars, event indicators, and additional explanatory variables can be supplied.
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Before fitting, verify:
- Timestamps are real datetime values, sorted chronologically, and have the intended time zone.
- Duplicate timestamps are removed or resolved.
- Missing timestamps and missing target values have an explicit treatment.
- The actual spacing between observations matches the assumed frequency.
- Every regressor required for the forecast horizon is known in advance or forecast separately.
- Rolling features and joins do not use information that would have been unavailable at the forecast cutoff.
Install Greykite
Greykite 1.1.0 declares Python >=3.10 and lists Python 3.10, 3.11, and 3.12 classifiers. The installation guide recommends a Python 3.10 environment and discusses Linux, macOS, and Windows.
- Create an isolated environment:
python -m venv .venv - Activate it:
# macOS/Linux source .venv/bin/activate # Windows PowerShell .venvScriptsActivate.ps1 - Upgrade packaging tools and install the package:
python -m pip install --upgrade pip setuptools wheel python -m pip install greykite
Prophet became optional beginning with Greykite 0.2.0. The older installation page mentions testing with prophet==1.0.1 and warns that newer Prophet versions were unsupported by that documentation. Treat the integration as version-sensitive: install Greykite first, add Prophet only when needed, and verify the exact dependency combination for your release.
Source: Greykite installation guide.
Common installation failures
- Unsupported interpreter: create a fresh Python 3.10–3.12 environment.
- Scientific-package build errors: upgrade
pip,setuptools, andwheel, then retry in the clean environment. - Prophet conflicts: install the optional integration separately and pin the working versions.
- Contaminated environment: remove the virtual environment and recreate it instead of mixing unrelated package versions.
Build a first forecast
The following example uses Greykite’s bikesharing sample data, a 24-step horizon, and nominal 95% coverage. These are demonstration settings, not universal recommendations.
from greykite.common.data_loader import DataLoader
from greykite.framework.templates.autogen.forecast_config import (
ForecastConfig,
MetadataParam,
)
from greykite.framework.templates.forecaster import Forecaster
from greykite.framework.templates.model_templates import ModelTemplateEnum
df = DataLoader().load_bikesharing().tail(24 * 90)
config = ForecastConfig(
metadata_param=MetadataParam(
time_col="ts",
value_col="count",
),
model_template=ModelTemplateEnum.AUTO.name,
forecast_horizon=24,
coverage=0.95,
)
result = Forecaster().run_forecast_config(df=df, config=config)
forecast = result.forecast
backtest = result.backtest
grid_search = result.grid_search
model = result.model
timeseries = result.timeseriesThe result contains the future forecast, historical backtest output, grid-search information, fitted-model details, and a processed time-series object used by plotting and diagnostics. Inspect the installed release’s schema because output columns and object details can change.
Use your own dataframe
import pandas as pd
from greykite.framework.templates.autogen.forecast_config import MetadataParam
df = pd.DataFrame({
"ts": pd.date_range("2025-01-01", periods=100, freq="D"),
"y": range(100),
})
df["ts"] = pd.to_datetime(df["ts"])
df = df.sort_values("ts")
assert df["ts"].is_unique
assert df["y"].notna().all()
metadata = MetadataParam(time_col="ts", value_col="y")ts and y are only example names. Set time_col and value_col to your actual columns.
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Model templates: AUTO or explicit Silverkite?
AUTO is a convenient starting template that reduces configuration work. It does not guarantee the best out-of-sample model and does not replace data cleaning, baseline comparisons, leakage checks, or backtesting. SILVERKITE selects the Silverkite template explicitly; other templates are tuned for different frequencies, horizons, and data patterns.
- Start with
AUTOand a naive or seasonal-naive baseline. - Run a backtest that matches the real forecast horizon.
- Inspect residuals, component plots, and failure periods.
- Move to an explicit Silverkite configuration when you need control over features or changepoints.
- Tune only after the evaluation design reflects deployment.
Validate forecasts correctly
Use time-ordered evaluation, not a random train/test split. Rolling-origin or expanding-window backtesting shows how the model behaves when trained on the past and asked to predict the next operational horizon.
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- Match the horizon to the decision: 24 hourly steps is not equivalent to a 90-day planning forecast.
- Compare with naive and seasonal-naive baselines.
- Evaluate several historical periods, including promotions, holidays, outages, and regime changes.
- Report point-forecast metrics separately from interval quality.
- Check residual bias, autocorrelation, outliers, and performance by segment or time period.
A coverage=0.95 setting requests a nominal 95% prediction interval. It does not prove that 95% of future observations will fall inside it. Measure empirical coverage and interval width on historical backtests, especially when variance changes, data is sparse, outliers occur, or structural breaks are present.
Events, holidays, and regressors
External variables can improve forecasts when they represent information genuinely available at prediction time. Useful examples include marketing campaigns, product launches, price changes, weather forecasts, stockouts, scheduled maintenance, public holidays, and company events.
| Regressor type | Production implication |
|---|---|
| Known in advance | Usually suitable when the future calendar, promotion, or maintenance schedule is reliable. |
| Forecast separately | Usable only if the additional forecasting step is evaluated and operationalized. |
| Unknown future value | Cannot be passed as if known; doing so creates an unusable pipeline or leakage. |
| Target-derived proxy | Must be calculated using only information available before each forecast cutoff. |
Common leakage examples include joining realized future sales, calculating a rolling feature across the cutoff, or using revised data that was unavailable when the original forecast would have been produced.
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Anomaly detection with Greykite AD
Greykite 1.1.0 describes Greykite AD as an extension for monitoring metrics and tuning anomaly thresholds using alert-rate information, anomaly labels, precision/recall objectives, and business-impact filters.
An ordinary prediction interval asks whether an observation is unusual under the forecasting model. An anomaly detector additionally asks whether an alert is operationally useful. A statistically unusual point may be harmless, while a smaller deviation during a critical process may deserve attention. Validate thresholds against labeled incidents or an agreed alert budget where possible.
Production checklist
- Pin the Greykite version and dependency set.
- Save the forecast configuration, feature definitions, holiday calendars, time zone, training cutoff, and horizon.
- Monitor data freshness, missingness, duplicate timestamps, and frequency regularity.
- Record forecasts and compare them with actuals when they arrive.
- Track drift, changepoints, residual bias, and interval coverage.
- Re-run backtests after major data, feature, or dependency changes.
- Test serialization and deployment behavior in the target runtime.
A LinkedIn research paper reports deployment across more than 20 LinkedIn use cases. That is evidence of use in LinkedIn’s environment, not a universal performance or scalability guarantee.
Source: Greykite research paper.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Strengths and trade-offs
| Criterion | Greykite implication |
|---|---|
| Interpretability | Feature-based models, component plots, and summaries are useful advantages. |
| Automation | Templates and AUTO reduce setup, but validation remains necessary. |
| Flexibility | Supports trend, seasonality, changepoints, events, autoregression, and regressors. |
| Data requirements | Works best with clean, timestamped, structured series on a stable time grid. |
| Dependencies | Use isolated, pinned environments; optional integrations can introduce conflicts. |
| Ecosystem freshness | PyPI’s latest listed release is 1.1.0 from February 20, 2025; publication history alone does not prove active development or abandonment. |
| Deep learning | Not the project’s central design. |
| License | BSD 2-Clause open-source license. |
Greykite alternatives
| Alternative | Consider it when |
|---|---|
| StatsForecast | You need fast statistical models across many univariate series. |
| sktime | You want a broad unified ecosystem for forecasting, reduction, classification, and regression; its repository lists Python 3.10–3.13 support. |
| Prophet | You want a straightforward trend, seasonality, and holiday API. Greykite’s Prophet interface is version-sensitive. |
| NeuralForecast | You are specifically experimenting with neural forecasting architectures; PyPI lists release 3.1.7 dated April 10, 2026. |
| Custom statsmodels or scikit-learn pipeline | You need a smaller dependency surface or complete control over feature engineering and deployment. |
Managed neural or foundation-model services may fit teams that need hosted infrastructure and have enough series volume to justify vendor cost. They are not automatically more accurate, interpretable, or reproducible than a properly validated Silverkite model.
Is Greykite right for you?
- Choose it when interpretable business forecasting, calendar effects, changepoints, regressors, backtesting, and plotting are central requirements.
- Be cautious when you need immediate support for the newest Python release, a rapidly evolving ecosystem, minimal dependencies, or guaranteed compatibility with current Prophet.
- Look elsewhere when data is highly irregular and event-driven without a stable grid, future regressors are unavailable, or your primary goal is state-of-the-art deep-learning or foundation-model research.
- For large panels, compare the framework’s runtime and operational pattern with a library designed specifically for high-volume statistical or global forecasting.
FAQ
Is Greykite the same as GrayKite?
No. The package and repository are named greykite. GreyKite and GrayKite are spelling variants that can cause search confusion.
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Is Greykite still maintained?
PyPI lists version 1.1.0, uploaded February 20, 2025, while the documentation index still labels 1.0.0 as latest documentation. Those facts show release history but do not, by themselves, establish the project’s current development activity.
Does Greykite work with Python 3.13?
PyPI metadata for 1.1.0 declares Python 3.10 or newer and lists classifiers through 3.12. Do not assume Python 3.13 compatibility without testing the complete dependency set.
Is Greykite free?
Yes. Greykite is an open-source BSD 2-Clause package with no paid Greykite plan identified in the cited project sources.
Can it forecast multiple time series?
The broader framework and production patterns can be used for multiple related series, but each deployment should be designed and benchmarked for its panel size, shared features, and operational constraints.
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