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Muntaha Nasir
STATISTICS

Time Series Analysis
In time series forecasting, what is the purpose of using a holdout sample?
A. To train the model.
B. To validate the model.
C. To test the model.
D. To tune the model.
Muntaha Nasir
STATISTICS

Time Series Analysis
Which method is used to detect structural breaks in a time series?
A. Chow test.
B. ADF test.
C. LjungBox test.
D. DurbinWatson test.
Muntaha Nasir
STATISTICS

Time Series Analysis
What is the key difference between an ARIMA and a SARIMA model?
A. ARIMA handles seasonality, SARIMA does not.
B. SARIMA handles seasonality, ARIMA does not.
C. ARIMA handles trend, SARIMA does not.
D. SARIMA handles trend, ARIMA does not.
Muntaha Nasir
STATISTICS

Time Series Analysis
Which type of plot is useful for identifying the presence of seasonality in a time series?
A. ACF plot.
B. PACF plot.
C. Scatter plot.
D. Seasonal subseries plot.
Muntaha Nasir
STATISTICS

Time Series Analysis
What does 'RMSE' stand for in the context of model evaluation?
A. Root Mean Square Error.
B. Random Mean Square Error.
C. Relative Mean Square Error.
D. Residual Mean Square Error.
Muntaha Nasir
STATISTICS

Time Series Analysis
In a time series, what does 'differencing' aim to achieve?
A. Remove seasonality.
B. Remove trend.
C. Stabilize variance.
D. Remove autocorrelation.
Muntaha Nasir
STATISTICS

Time Series Analysis
What does the term 'backshift operator' refer to in time series analysis?
A. A method to remove trend.
B. A method to shift the series forward.
C. A method to shift the series backward.
D. A method to remove seasonality.
Muntaha Nasir
STATISTICS

Time Series Analysis
Which method is suitable for capturing longrange dependencies in time series data?
A. ARIMA.
B. GARCH.
C. LSTM.
D. Exponential Smoothing.
Muntaha Nasir
STATISTICS

Time Series Analysis
In time series analysis, what does 'white noise' refer to?
A. A series with a predictable pattern.
B. A series with no autocorrelation.
C. A series with high variance.
D. A series with seasonality.
Muntaha Nasir
STATISTICS

Time Series Analysis
What is the role of 'hyperparameter tuning' in time series forecasting?
A. To select the best model.
B. To improve model accuracy.
C. To handle missing data.
D. To identify seasonality.
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