Forecasting at scale
Author and article information
Abstract
Forecasting is a common data science task that helps organizations with capacity planning, goal setting, and anomaly detection. Despite its importance, there are serious challenges associated with producing reliable and high quality forecasts — especially when there are a variety of time series and analysts with expertise in time series modeling are relatively rare. To address these challenges, we describe a practical approach to forecasting “at scale” that combines configurable models with analyst-in-the-loop performance analysis. We propose a modular regression model with interpretable parameters that can be intuitively adjusted by analysts with domain knowledge about the time series. We describe performance analyses to compare and evaluate forecasting procedures, and automatically flag forecasts for manual review and adjustment. Tools that help analysts to use their expertise most effectively enable reliable, practical forecasting of business time series.
Cite this as
2017. Forecasting at scale. PeerJ Preprints 5:e3190v2 https://doi.org/10.7287/peerj.preprints.3190v2Author comment
We updated the URL for our open source repository because that has changed recently.
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Additional Information
Competing Interests
Sean J Taylor and Benjamin Letham are both employees at Facebook, Inc.
Author Contributions
Sean J Taylor analyzed the data, wrote the paper, prepared figures and/or tables, performed the computation work, reviewed drafts of the paper.
Benjamin Letham analyzed the data, wrote the paper, prepared figures and/or tables, performed the computation work, reviewed drafts of the paper.
Data Deposition
The following information was supplied regarding data availability:
All code is available in our open source repository: https://github.com/facebookincubator/prophet
Funding
The authors received no funding for this work.
