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 quickstart.

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:

• Silverkite (Greykite’s flagship algorithm)
• Facebook Prophet
• Auto Arima

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.
• ReconcileAdditiveForecasts() - adjust a set of forecasts to satisfy inter-forecast additivity constraints.

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 column in `df`
)
forecaster = Forecaster()  # creates forecasts and stores the result
forecaster.run_forecast_config(
     df=df,
     config=ForecastConfig(
         model_template=ModelTemplateEnum.AUTO.name,  # automatically selects model parameters
         forecast_horizon=365,  # forecasts 365 steps ahead
         coverage=0.95,         # 95% prediction intervals
         metadata_param=metadata
     )
 )
# Access the result
forecaster.forecast_result
# ...

For a demo, please see our quickstart.

Setup and Installation

Greykite is available on Pypi and can be installed with pip:

pip install greykite

For more installation tips, see installation.

Documentation

Please find our full documentation here.

Learn More

• Website
• Paper
• Blog post

Citation

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

@misc{reza2021greykite-github,
  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    = {https://github.com/linkedin/greykite},
  year   = {2021}
}

@misc{reza2021greykite-paper,
  author = {Reza Hosseini and
            Kaixu Yang and
            Albert Chen and
            Sayan Patra},
  title  = {A flexible forecasting model for production systems},
  url    = {https://arxiv.org/abs/2105.01098},
  year   = {2021}
}

License

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