Time Series Forecasting Seasonal type
Each time series can be decomposed in at least three elements:


Trend
Seasonal component
Noise


An additive model can be explained as:

y = Trend + Seasonal + Noise


while a multiplicative component as:

y = Trend * Seasonal * Noise


The main difference is that in the case of additive models all three components impact your dependent variable independently. In case of multiplicative models instead, the impact of each component is dependent from the others.

The choice of the "right" model depends on the problem at hand. If you assume that the Seasonal effect is independent from the level of the Trend, then use an additive model. If you think the value of the Trend is going to affect the magnitude of the Seasonal effect, then go for the multiplicative one.

Hope this helps, otherwise let me know.