When performing time series decomposition , which method separates data into additive components?

Explanation: Additive decomposition breaks time series data into three components: trend, seasonality, and residuals, This method is used when variations in data remain constant over time. For instance, in weather data, an additive model would work if seasonal effects (like winter temperatures) are independent of the overall temperature trend. Option A: ARIMA focuses on autoregressive and moving average properties rather than decomposition. Option B: Multiplicative decomposition is a separate method used when variations grow or shrink proportionally to the trend. Option D: Exponential decomposition is not a recognized decomposition method in time series analysis. Option E: STL decomposition includes Loess smoothing but does not strictly follow the additive framework.