The measures of seasonal variations are called seasonal indices which are expressed either in terms of absolute values viz : S = Y – (T + C + I) under the additive model, or in terms of percentages of the remaining components viz : S = *TSCI/ TCI* × 100, or *Y/TCI *× 100 under the multiplicative model. It may be noted that in order to compute the seasonal variations in a time series, the data must be expressed season wise (i.e. month wise, quarters wise or week wise etc.). They cannot be computed from the data given in annual fashion for that they do not exhibit any seasonal variations it them. Thus, for the monthly data, there would be 12 seasonal indices in a year, for the quarterly data 4 seasonal indices in a year and for the weekly data 52 seasonal indices in a year.

**Different types of seasonal Index**

A seasonal index is mainly of two types:

(i) Specific seasonal index, and (ii) Typical seasonal index.

A specific seasonal index is one which is obtained for each part of year i.e. for each month, quarter, or week. These indices are computed as percentage of their periodical average i.e. monthly, quarterly, or weekly averages as the case may be.

A Typical seasonal index**, **on the other hand, is one which is obtained for a year by averaging a number of specific seasonal indices.

**Methods of computation**

There are various methods of computing the seasonal variations in a time series. The most important and popular ones among them are the following:

- Method of Simple average
- Method of Ration to trend
- Method of Ratio to moving average
- Method of Link relatives or Pearson’s method.