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    Question

    When performing time series decomposition , which

    method separates data into additive components?
    A ARIMA decomposition Correct Answer Incorrect Answer
    B Multiplicative decomposition Correct Answer Incorrect Answer
    C Additive decomposition Correct Answer Incorrect Answer
    D Exponential decomposition Correct Answer Incorrect Answer
    E Seasonal decomposition using Loess (STL) Correct Answer Incorrect Answer

    Solution

    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.

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