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. Co-authors: Reza Hosseini, Albert Chen, Kaixu Yang, Sayan Patra, Rachit Arora, and Parvez Ahammad.
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.
The article then describes in detail:
- Forecasting applications
- Algorithm design
- Case studies
- User experience
- Evaluation
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. Very interesting read!
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