Which forecasting method weights recent data more heavily?

Which forecasting method weights recent data more heavily?

• A. Naive
• B. ARIMA
• C. Exponential Smoothing
• D. Moving Average

Answer: (C)

Exponential smoothing places the most weight on the most recent observations, with weights that decay for older data. In its simple form, the forecast combines the latest actual value and the previous forecast: F_{t+1} = alpha * A_t + (1 - alpha) * F_t. The smoothing parameter alpha (between 0 and 1) controls how strongly the latest data point influences the forecast: larger alpha means the most recent data dominates more, while smaller alpha spreads influence more across earlier data. Naive simply copies the last value, so it doesn’t weight a history at all. Moving average gives equal weight to all points in the chosen window, so newer data aren’t favored. ARIMA uses a model with autoregressive and moving-average terms, where weights come from model parameters rather than a straightforward rule that emphasizes the latest observation. Thus, exponential smoothing is the method that weights recent data more heavily.

Exponential smoothing places the most weight on the most recent observations, with weights that decay for older data. In its simple form, the forecast combines the latest actual value and the previous forecast: F_{t+1} = alpha * A_t + (1 - alpha) * F_t. The smoothing parameter alpha (between 0 and 1) controls how strongly the latest data point influences the forecast: larger alpha means the most recent data dominates more, while smaller alpha spreads influence more across earlier data.

Naive simply copies the last value, so it doesn’t weight a history at all. Moving average gives equal weight to all points in the chosen window, so newer data aren’t favored. ARIMA uses a model with autoregressive and moving-average terms, where weights come from model parameters rather than a straightforward rule that emphasizes the latest observation.

Thus, exponential smoothing is the method that weights recent data more heavily.