A seasonal pattern is a rise and fall in the data values that repeats regularly over the same time period. For example, orders at an auto parts store are low each Monday, increase during the week, and peak each Friday. Seasonal patterns always have a fixed and known period. In contrast, cyclic movements are cycles of rising and falling data values that do not repeat at regular intervals. Typically, cyclic movements are longer and more variable than seasonal patterns.
You can use a time series analysis to model patterns and generate forecasts. For more information on which analysis to use, go to Which time series analysis should I use?.
- Seasonal pattern
- These data show a seasonal pattern. The pattern repeats every 12 months.
- Cyclic movements
- These data show cyclic movements. The cycles do not repeat at regular intervals and do not have the same shape.
- Random variation
- These data show random variation. There are no patterns or cycles.