Date of this Version
Purpose: Commentary on M4-Competition and findings to assess the contribution of data models—such as from machine learning methods—to improving forecast accuracy.
Methods: (1) Use prior knowledge on the relative accuracy of forecasts from validated forecasting methods to assess the M4 findings. (2) Use prior knowledge on forecasting principles and the scientific method to assess whether data models can be expected to improve accuracy relative to forecasts from previously validated methods under any conditions.
Findings: Prior knowledge from experimental research is supported by the M4 findings that simple validated methods provided forecasts that are: (1) typically more accurate than those from complex and costly methods; (2) considerably more accurate than those from data models.
Limitations: Conclusions were limited by incomplete hypotheses from prior knowledge such as would have permitted experimental tests of which methods, and which individual models, would be most accurate under which conditions.
Implications: Data models should not be used for forecasting under any conditions. Forecasters interested in situations where much relevant data are available should use knowledge models.
Armstrong, J. S., & Green, K. C. (2019). Why Didn’t Experts Pick M4-Competition Winner?. Retrieved from https://repository.upenn.edu/marketing_papers/431
Date Posted: 14 February 2019