Time series often display a long-run trend. Some time series also display a strong, repeating seasonal pattern.
Regression methods can be used to model the trend and seasonal variation in a time series. Indicator variables can be used to model the seasons in a time series. When indicator variables are used in a regression applied to untransformed data, the indicator variables capture additive seasonal effects.
Transformations, such as the logarithm, can simplify the regression modeling process for trend and seasonal fitting. Seasonal indicator variables used in the modeling of logged data capture multiplicative seasonal effects.
Examine the residuals from a regression-based time series model to see if there is any evidence of systematic patterns, such as autocorrelation, that are not adequately captured by the model.