Time series analysis is the analysis of time-dependent data. The two most important factors in this analysis are the analysis of trends and the analysis of seasonality.

The analysis of trends can be considered as determining the function around which the data is distributed. Using the fact that data is dependent on time, this function can be determined using regression. Many phenomena have a linear trend line, whereas others may not follow a linear pattern.

The analysis of the seasonality tries to detect regular patterns occurring in time repeatedly, such as higher sales before Christmas and so on. To detect a seasonal pattern, it is essential to divide data into the different seasons in such a way that a pattern reoccurs in the same season. This division can divide a year into months, a week into days or into workdays and the weekend, and so on. An appropriate division into seasons and analyzing patterns in those is the key to good seasonal analysis.

Once trend and seasonality have been analyzed in the data, the combined result is a predictor for the pattern that the time-dependent data will follow in the future.