  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.
