The Components of Time Series

Four separate components – trend, cyclical, seasonal and irregular – combine to provide specific values for the time series.

Trend Component

Trend is the underlying long-term movement over time in the value of the data recorded. This shifting or trend is usually the result of long-term factors such as changes in the population, demographic characteristics of the population, technology and consumer preferences.

Seasonal Variations

Are short-term fluctuations in recorded values, due to different circumstances, which affect results at different times of the year, on different days of the week, at different times of day, or whatever.

Examples of Seasonal Variation are as follows.

• Sales of ice cream will be higher in summer than in winter, and sales of overcoats will be higher in autumn than in spring.
• Shops might expect higher sales shortly before Christmas or in their winter and summer sales.
• Sales might be higher on Friday and Saturday than on Monday.
• The telephone network may be heavily used at a certain times of the day (such as mid-morning and mid-afternoon) and much less used at other times (such as in the middle of the night)

Cyclical Variation

These are medium-term changes in results caused by circumstances which repeat in cycles. In business, cyclical variations are commonly associated with economic cycles, successful booms and slumps in the economy. Economic cycles may last a few years. Cyclical Variations are longer term than seasonal variations.

Random Factors

These are disturbances due to ‘everyday’ unpredictable influences, such as weather conditions, illness, transport breakdowns and so on.