Using first differences (lag-1 differencing) is useful to
a:) Remove quarterly seasonality from a series.
b:) Remove both trend and seasonality in one step.
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c:) Remove a trend, especially if the shape of the trend is not fixed throughout the time period. d:) Make a trend easier to visualize.
7:) The greatest smoothing effect is obtained by using
a:) a moving average based on a small number of periods.
b:) the root-mean-square error.
c:) the barometric method.
d:) exponential smoothing with a small weight value
. 8:) When using simple exponential smoothing, if we want our forecast to be very responsive to recent values in the time series, the value of alpha should be:
a:) small b:) moderate c:) large d:) zero
9:) The R function ets (which stands for error, trend, seasonality) is used for simple exponential smoothing. What happens if you do not specify a value for alpha when executing the ets function?
a:) R will calculate the optimal value of alpha using the maximum likelihood method (effectively minimizing the RMSE in the training period).
b:) R will return an error because the function is not completely specified.
c:) R will assume an alpha equal to 0.5.
d:) R will use the last value of alpha that was specified.
10:) The Holt-Winter exponential smoothing model is most appropriate for a time series that has
a:) A trend, but no seasonality
b:) Seasonality, but no trend
c:) Both trend and seasonality
d:) Neither trend nor seasonality