Greykite: A flexible, intuitive, and fast forecasting library

In this blog post, we introduce the Greykite library, an open source Python library developed to support LinkedIn’s forecasting needs. Its main forecasting algorithm, called Silverkite, is fast, accurate, and intuitive, making it suitable for interactive and automated forecasting at scale. We will start by describing a few applications, and then walk through the algorithm design and user experience. For more technical details, please refer to this paper.

Accurate knowledge about the future is helpful to any business. Time series forecasts can provide future expectations for metrics and other quantities that are measurable over time.

While domain knowledge and expert judgment can sometimes produce accurate forecasts, algorithmic automation enables scalability and reproducibility, and may improve accuracy. Algorithmic forecasts can be consumed by additional algorithms downstream to make decisions or derive insights.

To support LinkedIn’s forecasting needs, we developed the Greykite Python library. Greykite contains a simple modeling interface that facilitates data exploration and model tuning. Its flagship algorithm, Silverkite, is highly customizable, with tuning parameters to capture diverse time series characteristics. The output is interpretable, allowing visualizations of the trend, seasonality, and other effects, along with their statistical significance.

Leave a Comment