deseasonalizing the data, using any forecasting model, then reseasonalizing the

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deseasonalizing the data, using any forecasting model, then reseasonalizing the data You will always get more accurate forecasts by using more complex forecasting methods If the span of a moving average is large - say. 12 months - then few observations go into each average, and extreme values have relatively large effect on the forecasts To deseasonalize an observation (assuming a multiplicative model of seasonality) multiply it by the appropriate seasonal index In a multiplicative seasonal model we multiply a base forecast by an appropriate seasonal index. These indexes one for each season typically average to 1

Explanation / Answer

35)

that is false because a moving average is large it depends on how much large do you need

36)

true!

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