Vetting the Makridakis Dataset: Further Indications of the Robustness of the Rule Based Forecasting Model
Context: The year 2022 marks the 30th anniversary of Collopy and Armstrong’s The Rule Based Forecasting [RBF] Expert Systems Model. Over the last three decades, there has been a plethora of research reports—truly a research Cornucopia—spawned by this very unique, effective, and ground-breaking forecasting system. Focus: The purpose of this research note is to: (i) Briefly, remind the forecasting community of the excellent pre-model-launch vetting used by Collopy and Armstrong [C&A] to form their RBF-model. Important is: their vetting protocols readily generalize to most modeling domains, and (ii) Offer a “re-vetting” analysis of the M-Competition dataset used by C&A that addresses their comment: “This study also used long calibration series - - -; rule-based forecasting benefits from long series because it uses information about patterns in the data. We do not know how the procedure will perform for short series.” [p. 1403[Bolding Added]]. Results: We trimmed selected series from the M-Competition to arrive at 165-series all of which had 13-time series points for the OLS Regression-fit [OLS-R] & three panel-points as holdbacks. We found that: (i) there is evidence that these trimmed-series likely have inferentially differentiable variance profiles compared to the performance profiles reported by C&A, and (ii) despite this, these trimmed-segments did not seem to compromise the C&A’s parametrization of the RBF Model in comparison to OLS-R forecasts. Finally, we suggest the need for an extension of the RBF Expert System re: (1-FPE) Confidence Intervals that would further enhance RBF-testing with respect to capture-rates and relative precision.
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