Which time series model is suitable when seasonal fluctuations do not depend on the level of the series?

The additive model is the suitable choice for a time series when seasonal fluctuations do not depend on the level of the series. This model implies that the seasonal variations remain constant over time, regardless of the overall level of the data. In other words, the seasonal effect is added directly to the other components of the series, such as trend or irregular components.

For example, if you have a sales dataset that experiences seasonal fluctuations that are the same amount each year (e.g., an increase of 100 units during the holiday season), the additive model properly accommodates this by adding a fixed seasonal component to the base level of sales. This characteristic makes it ideal for data where the amplitude of the seasonal variation does not change as the level of the data increases or decreases.

In contrast, the multiplicative model is used when seasonal effects vary in intensity depending on the level of the series, meaning that the fluctuations would be proportional to the data’s current level. Seasonal models alone are a broader reference that might not specify the relationships clearly, while linear models focus on trends without considering seasonality specifically. Thus, the additive model aligns perfectly with scenarios where seasonal changes are in a fixed amount, independent of the series' level.