The degree to which a model explains (statistically speaking) variations in the calibration data. Fit is likely to be misleading as a criterion for selecting and developing forecasting models, because it typically has only a weak relationship to ex ante forecast accuracy (Armstrong 2001d). Fit tends to favor complex models, and these models often do not hold up in forecasting, especially when using time-series data. Nevertheless, Pant and Starbuck (1990) found a modest relationship between fit (when using MAPE) and short-term forecasts for 13 extrapolation methods. It is more relevant when working with cross-sectional data.
- Armstrong, J. S. (2001d), “Evaluating forecasting
methods,” in J. S. Armstrong (ed.), Principles of Forecasting. Norwell,
MA: Kluwer Academic Press.
- Pant, P. N. & W. H. Starbuck (1990), “Innocents in
the forest: Forecasting and research methods,” Journal of Management, 16,
433-460.