In statistics, many time series exhibit cyclic variation known as seasonality, seasonal variation, periodic variation, or periodic fluctuations. This variation can be either regular or semi-regular.Seasonal variation is a component of a time series which is defined as the repetitive and predictable movement around the trend line in one year or less. It is detected by measuring the quantity of interest for small time intervals, such as days, weeks, months or quarters.Organizations facing seasonal variations, like the motor vehicle industry, are often interested in knowing their performance relative to the normal seasonal variation. The same applies to the ministry of employment which expects unemployment to increase in June because recent graduates are just arriving into the job market and schools have also been given a vacation for the summer. That unemployment increased as predicted is a moot point; the relevant factor is whether the increase is more or less than expected.Organizations affected by seasonal variation need to identify and measure this seasonality to help with planning for temporary increases or decreases in labor requirements, inventory, training, periodic maintenance, and so forth. Apart from these considerations, the organizations need to know if the variation they have experienced has been more or less than the expected, given the usual seasonal variations.EXAMPLESFor example, retail sales tend to peak for the Christmas season and then decline after the holidays. So time series of retail sales will typically show increasing sales from September through December and declining sales in January and February.Seasonality is quite common in economic time series. It is also very common in geophysical and ecological time series. A notable example is the concentration of atmospheric carbon dioxide: it is at a minimum in September and October, at which point it begins to increase, reaching a peak in April/May, before declining. Another example consists of the famous Milankovitch cycles.