Time-series data that tend to go through recurring increases and decreases. See also business cycle. This term is generally not used for seasonal variations within a year. Although it is difficult to forecast cycles, knowledge that a time series is subject to cycles may be useful for selecting a forecasting method and for assessing uncertainty. (See also long waves.) See Armstrong
(2001c) and Armstrong, Adya and Collopy (2001).
- Armstrong, J. S. (2001c), “Extrapolation for time-series
and cross-sectional data,” in J. S. Armstrong (ed.), Principles of
Forecasting. Norwell, MA: Kluwer Academic Press.
- 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