Which of the following best describes the difference between simple moving averages and exponential smoothing in forecasting?

Explanation: Exponential smoothing techniques assign exponentially decreasing weights to older observations, allowing the model to prioritize recent trends and adapt to changes quickly. This feature makes it more effective for dynamic datasets. In contrast, simple moving averages calculate the average over a fixed window, giving equal importance to all points within that window, which can result in lagged responses to new trends. Exponential smoothing is ideal for forecasting in volatile environments where recent changes are more indicative of future outcomes. Option A: Exponential smoothing does consider all past data, but moving averages can also include multiple windows. Option B: Moving averages give equal weight within the window, while exponential smoothing emphasizes recent data. Option C: Both methods can incorporate seasonality adjustments in advanced forms.       Option D: Neither method strictly requires decomposition, though they benefit from it.