The American Statistician· 2017Q1
Forecasting at Scale
- 2,333citations
- Q1SCImago
- 2017year
Short summary
A new approach combines configurable, interpretable regression models with analyst-in-the-loop performance analysis to enable reliable, large-scale business time series forecasting.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces a practical approach to forecasting "at scale" for business time series.
- Combines configurable, modular regression models with interpretable parameters.
- Incorporates analyst-in-the-loop performance analysis for manual review and adjustment.
- Aims to enable reliable forecasting despite a scarcity of time series modeling experts.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. The American Statistician, 2017 · DOI ↗
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Field: Management Science and Operations Research
Management Science and Operations ResearchDecision Sciences