Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Watch now.
LinkedIn today open-sourced Greykite, a Python library for long- and short-term predictive analytics. Greykite’s main algorithm, Silverkite, delivers automated forecasting, which LinkedIn says it uses for resource planning, performance management, optimization, and ecosystem insight generation.
For enterprises using predictive models to forecast consumer behavior, data drift was a major challenge in 2020 due to never-before-seen circumstances related to the pandemic. This being the case, accurate knowledge about the future remains helpful to any business. Automation, which enables reproducibility, may improve accuracy and can be consumed by algorithms downstream to make decisions.
For example, LinkedIn says that Silverkite improved revenue forecasts for 1-day ahead and 7-day ahead, as well as Weekly Active User forecasts for 2-week ahead. Median absolute percent error for revenue and Weekly Active User forecasts grew by more than 50% and 30%, respectively.
Greykite provides time series tools for trends, seasonality, holidays, and more so that users can fit the AI models of their choice. The library provides exploratory plots and templates for tuning, which define regressors based on data characteristics and forecast requirements like hourly short-term forecast and daily long-term forecast. Tuning knobs provided by the templates reduce the search to find a satisfactory forecast. And the Greykite library has flexibility to customize a model template for algorithms, letting users label (and specify whether to ignore or adjust) known anomalies.
Intelligent Security Summit
Learn the critical role of AI & ML in cybersecurity and industry specific case studies on December 8. Register for your free pass today.
Greykite, which provides outlier detection, can also select the optimal model from multiple candidates using past performance data. Instead of tuning each forecast separately, users can define a set of candidate forecast configurations that capture different types of patterns. Lastly, the library provides a summary that can be used to assess the effect of individual data points. For example, Greykite can check the magnitude of a holiday, see how much a changepoint affected the trend, or show how a certain feature might be beneficial to a model.
With Greykite, a “next 7-day” forecast trained on over 8 years of daily data takes only a few seconds to produce forecasts. LinkedIn says that its whole pipeline, including automatic changepoint detection, cross-validation, backtest, and evaluation, completes in under 45 seconds.
“The Greykite library provides a fast, accurate, and highly customizable algorithm — Silverkite — for forecasting. Greykite also provides intuitive tuning options and diagnostics for model interpretation. It is extensible to multiple algorithms, and facilitates benchmarking them through a single interface,” the LinkedIn research team wrote in a blog post. “We have successfully applied Greykite at LinkedIn for multiple business and infrastructure metrics use cases.”
The Greykite library is available on GitHub and PyPI, and it joins the many other tools LinkedIn has open-sourced to date. They include Iris, for managing website outages; PalDB, a low-key value store for handling side data; Ambry, an object store for media files; GDMix, a framework for training AI personalization models; LiFT, a toolkit to measure AI model fairness; and Dagli, a machine learning library for Java.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Discover our Briefings.