In a time series model, what component accounts for multiyear cycles?

The cyclical component is associated with multiyear cycles within a time series model. This component reflects the long-term fluctuations in data that occur at irregular intervals, often influenced by economic or other external factors. Unlike the seasonal component, which exhibits regular, predictable changes that repeat over shorter periods (such as monthly or quarterly), the cyclical component can span several years or more and does not have a fixed frequency.

This means that when analyzing data trends over time, the cyclical component captures the effects of broader, longer-term forces that may take a more extended period to manifest, such as business cycles or significant economic conditions. In contrast, the trend component highlights the long-term progression of the data, while the random component deals with noise and unexplained variability that can occur at any time. Thus, recognizing the cyclical component is crucial for understanding and forecasting long-term behaviors in a dataset.