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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.
Who among the following likes Cauliflower?
Which of the following statement is correct?
Who among the following is an immediate neighbor of Pallav and Padma?
If Y starts walking towards west, after walking 30m, he takes a right turn of 12m, then what is the position of Y with respect to Q?
Eight friends, F, G, H, I, J, K, L and M are sitting in a line facing north. J is sitting at one of the corners. G is sitting between M and H. L is sitt...
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Which of the following statement is true?
Who is to the immediate right of J?
Which of the following statements is/are not true regarding I?
Which of the following options is a pair of men?