A series in which the historical trend extrapolation is opposite in direction to prespecified expectations of domain experts. For example, domain experts might think that the causal forces should drive the series up, but the historical trend is headed down. Contrary series can lead to large errors. Evidence to date suggests that statistical trend estimates should be ignored for contrary series (Armstrong and Collopy 1993). In addition, contrary series are expected to have asymmetric errors, even when expressed in logs (Armstrong and Collopy 2000). See Armstrong, Adya and Collopy (2001).
- Armstrong, J. S. & F. Collopy (1993), “Causal
forces: Structuring knowledge for time-series extrapolation,” Journal of
Forecasting, 12, 103-115. (Full
text)
- Armstrong, J. S. & F. Collopy (2001),
“Identification of asymmetric prediction intervals through causal forces,”
Journal of Forecasting (forthcoming).
- Armstrong, J. S., M. Adya & F. Collopy (2001),
“Rule-based forecasting: Using judgment in time-series extrapolation,” in J. S. Armstrong
(ed.), Principles of Forecasting. Norwell, MA: Kluwer Academic
Press.