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 Press.