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Answer :
B. False. The number of significant correlations in the PACF plot tells the value of q in an ARIMA(0,d,q) model.
To estimate d in an ARIMA(p,d,q) model, option C is correct.
B. False.
The naïve method forecast for period 7 in the given time series (1,7,2,7,2,1) would be 1.
False. If the difference between each consecutive term in a time series is constant, we call it a trend model.
B. MAPE. MAPE is not appropriate when dealing with time series
We can use either the ADF test or KPSS test to determine if d makes the time series stationary or not. If the time series is non-stationary, we increment d by 1 and repeat the test until we achieve stationarity.
B. False. The Augmented Dickey Fuller Test is used to determine whether a time series has a unit root or not, which in turn helps us in determining whether it is stationary or not. It does not prove randomness of residuals.
The naïve method forecast for a time series is simply the last observed value. Therefore, the naïve method forecast for period 7 in the given time series (1,7,2,7,2,1) would be 1.
False. If the difference between each consecutive term in a time series is constant, we call it a trend model.
True. If the difference between each consecutive term in a time series is random, we call it a random walk model.
The seasonal naïve method forecast for a time series is simply the last observed value from the same season in the previous year. Therefore, the seasonal naïve method forecast for period 8 in the given time series (4,1,3,2,5,1,2) would be 4.
A. subset
B. MAPE. MAPE is not appropriate when dealing with time series that have real zero values because of the possibility of division by zero, \which can lead to undefined values. RMSE, MAE, and MASE are suitable
for temperature time series.
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