LinkedIn releases a time-series forecasting library, Greykite, to simplify prediction for data scientists. The primary forecasting algorithm used in this library is Silverkite, which automates the forecasting. LinkedIn developed GrekKite to support its team make effective decisions based on the time-series forecasting models. As the library also helps interpret outputs, it can become a go-to tool for most time-series forecasting. LinkedIn also had, last year, released a Fairness Toolkit for explainability in machine learning.
Over the years, LinkedIn has been using the Greykite library to provide sufficient infrastructure to handle peak traffic, set business targets, and optimize budget decisions.
According to LinkedIn, the Silverkite algorithm architecture is shown in Figure 1. The green parallelograms represent model inputs, and the orange ovals represent model outputs. The user provides the input time series and any known anomalies, events, regressors, or changepoint dates. The model returns forecasts, prediction intervals, and diagnostics.
Often, time-series models fail to consider seasonality and other non-frequent events, making it difficult to predict the outcome precisely. The is where LinkedIn’s Greykite library assists data scientists while working with seasonality and holidays. Users can fit their models based on the requirements and effectively work with changepoints and seasonality.
Since the models not only forecast but also provide exploratory plots, templates for tuning, and explainable forecasts, the Greykite library can be used for quick prototyping and deployment at scale.
To benchmark the performance of LinkedIn’s Greykite library, the researchers used several datasets — Peyton-Manning Dataset, Daily Australia Temperature Dataset, and Bejing PM2.5 Dataset. Silverkite outperformed Prophet, Facebook’s open-source algorithm for forecasting, ran four times faster than the latter.
Currently, the LinkedIn Greykite library also supports Prophet and will add more open-source algorithms to allow data scientists to work on diverse forecasting requirements.