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If a process is ARIMA(0,d,q), number of significant correlations in ACF plot tells the value of q.

A. True

B. False

How to estimate d in ARIMA(p,d,q) model?

A. Take random guess and keep trying until you find the optimal solution.

B. First try d=0 and note the error. Then try d =1 and note the error and then try d=2 and not the error. whichever d gives you lowest error in ARIMA model, use that d.

C. Use ADF test or KPSS test to determine if d makes the time series stationary or not. If not, increment d by 1.

D. Use ACF and PACF to estimate approximate d.

Augmented Dickey Fuller Test is used to prove randomness of the residuals of a forecasting method.

A. True

B. False

Augmented Dickey Fuller Test is used to prove randomness of the residuals of a forecasting method.

A. True

B. False

What is the naïve method forecast of following time series (1,7,2,7,2,1) for period 7?

A. 7

B. 1

C. 2

D. 3/2

If the difference between each consecutive term in a time series is constant, we call it Drift Model.

True

False

If the difference between each consecutive term in a time series is random, we call it random walk model.

True

False

If data exhibits quarterly seasonality, what is the seasonal naïve method forecast of following time series (4,1,3,2,5,1,2) for period 8?

A. 3

B. 1

C. 5

D. 2

E. 4

33. What command allows sub setting (cutting the time series into a smaller time series) of a time series in R ?

A. subset

B. cut

C. window

D. view

Which method of measure error is NOT appropriate when forecasting temperature time series which can have a real zero value?

A. RMSE

B. MAPE

C. MAE

D. MASE

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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