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A python package for flexible forecasting

Project description

Greykite: A flexible, intuitive and fast forecasting library


Why Greykite?

The Greykite library provides flexible, intuitive and fast forecasts through its flagship algorithm, Silverkite.

Silverkite algorithm works well on most time series, and is especially adept for those with changepoints in trend or seasonality, event/holiday effects, and temporal dependencies. Its forecasts are interpretable and therefore useful for trusted decision-making and insights.

The Greykite library provides a framework that makes it easy to develop a good forecast model, with exploratory data analysis, outlier/anomaly preprocessing, feature extraction and engineering, grid search, evaluation, benchmarking, and plotting. Other open source algorithms can be supported through Greykite’s interface to take advantage of this framework, as listed below.

For a demo, please see our documentation.

Distinguishing Features

  • Flexible design
    • Provides time series regressors to capture trend, seasonality, holidays, changepoints, and autoregression, and lets you add your own.

    • Fits the forecast using a machine learning model of your choice.

  • Intuitive interface
    • Provides powerful plotting tools to explore seasonality, interactions, changepoints, etc.

    • Provides model templates (default parameters) that work well based on data characteristics and forecast requirements (e.g. daily long-term forecast).

    • Produces interpretable output, with model summary to examine individual regressors, and component plots to visually inspect the combined effect of related regressors.

  • Fast training and scoring
    • Facilitates interactive prototyping, grid search, and benchmarking. Grid search is useful for model selection and semi-automatic forecasting of multiple metrics.

  • Extensible framework
    • Exposes multiple forecast algorithms in the same interface, making it easy to try algorithms from different libraries and compare results.

    • The same pipeline provides preprocessing, cross-validation, backtest, forecast, and evaluation with any algorithm.

Algorithms currently supported within Greykite’s modeling framework:

Notable Components

Greykite offers components that could be used within other forecasting libraries or even outside the forecasting context.

  • ModelSummary() - R-like summaries of scikit-learn and statsmodels regression models.

  • ChangepointDetector() - changepoint detection based on adaptive lasso, with visualization.

  • SimpleSilverkiteForecast() - Silverkite algorithm with forecast_simple and predict methods.

  • SilverkiteForecast() - low-level interface to Silverkite algorithm with forecast and predict methods.

Usage Examples

You can obtain forecasts with only a few lines of code:

from greykite.framework.templates.autogen.forecast_config import ForecastConfig
from greykite.framework.templates.autogen.forecast_config import MetadataParam
from greykite.framework.templates.forecaster import Forecaster
from greykite.framework.templates.model_templates import ModelTemplateEnum

# df = ...  # your input timeseries!
metadata = MetadataParam(
    time_col="ts",     # time column in `df`
    value_col="y"      # value in `df`
forecaster = Forecaster()  # creates forecasts and stores the result
         # uses the SILVERKITE model template parameters,
         forecast_horizon=365,  # forecasts 365 steps ahead
         coverage=0.95,         # 95% prediction intervals
# Access the result
# ...

For a demo, check out our step-by-step quickstart and tutorials in the documentation.

Setup and Installation

pip install greykite


Please find our full documentation at here.

Learn More

  • Paper (link coming soon!)

  • Blog post (link coming soon!)


Please cite Greykite in your publications if it helps your research:

  author = {Reza Hosseini and
            Albert Chen and
            Kaixu Yang and
            Sayan Patra and
            Rachit Arora},
  title  = {Greykite: a flexible, intuitive and fast forecasting library},
  url    = {},
  year   = {2021}


Copyright (c) LinkedIn Corporation. All rights reserved. Licensed under the BSD 2-Clause License.

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