Greykite: Deploying Flexible Forecasting at Scale at LinkedIn

TL;DR

Greykite is an open-source Python library that provides accurate, interpretable, and flexible forecasts for diverse time series, successfully deployed at LinkedIn for various business and engineering applications.

Contribution

It introduces the Silverkite algorithm, a novel, fast, and interpretable forecasting method capable of modeling complex effects in time series data.

Findings

Excellent speed and accuracy on diverse datasets

Trusted by multiple teams at LinkedIn for resource planning and analysis

Enables self-serve data exploration and model interpretation

Abstract

Forecasts help businesses allocate resources and achieve objectives. At LinkedIn, product owners use forecasts to set business targets, track outlook, and monitor health. Engineers use forecasts to efficiently provision hardware. Developing a forecasting solution to meet these needs requires accurate and interpretable forecasts on diverse time series with sub-hourly to quarterly frequencies. We present Greykite, an open-source Python library for forecasting that has been deployed on over twenty use cases at LinkedIn. Its flagship algorithm, Silverkite, provides interpretable, fast, and highly flexible univariate forecasts that capture effects such as time-varying growth and seasonality, autocorrelation, holidays, and regressors. The library enables self-serve accuracy and trust by facilitating data exploration, model configuration, execution, and interpretation. Our benchmark results show excellent out-of-the-box speed and accuracy on datasets from a variety of domains. Over the past two years, Greykite forecasts have been trusted by Finance, Engineering, and Product teams for resource planning and allocation, target setting and progress tracking, anomaly detection and root cause analysis. We expect Greykite to be useful to forecast practitioners with similar applications who need accurate, interpretable forecasts that capture complex dynamics common to time series related to human activity.