Intro to Time Series
Additive seasonality refers to a situation in a time series where seasonal variations are constant and can be added directly to the trend component. This means that the seasonal effect does not change in magnitude or scale with the level of the data, allowing for straightforward modeling and interpretation of seasonal patterns. In this context, understanding how these seasonal fluctuations interact with other components like trend and noise is crucial for accurate forecasting.
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