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2017, PeerJ Preprints. DOI: 10.7287/peerj.preprints.3190
Forecasting at scale
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.
2022-04-19 21:09:00
#paper 10.7287/peerj.preprints.3190v1 Taylor, S. J., & Letham, B. (2017, August 25). Forecasting at Scale. PeerJ Prepr 17年Facebook开源的Prophet。原理不复杂,时序分成3个部分,趋势项,周期项,节假日项。之前用在我司的一个预测模型里面,但是最近算是正儿八经的把论文给读了。Prophet被诟病最多应该还是没啥理论,尤其是趋势项的部分分解过于粗暴了,把时序上的所有点,分解的所有项都看作是t的函数,确实带有一股工业界浓浓的ML气息。虽然粗暴,但不得不说使用体验却是很好。prophet特别适用于商业时间序列的预测,并且这个包中集成了很多方便使用的工具,例如可以方便地定义节假日,方便地定于周期,中间时间序列有缺失值也不仅要,集成了异常检测识别,模型评估方法,时间序列分解图,所以说,即使不是很了解理论的人,也能够很容易使用这个包,简单而言,对数据分析师,非常友好。
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