MCQs

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Overall MCQs for Reference

What does the “d” parameter represent in an ARIMA(p, d, q) model?
a. Order of auto-regression
b. Order of differencing
c. Order of moving average
d. Order of trend
Answer: b. Order of differencingscaler+2
Which ARIMA model component accounts for lagged forecast errors?
a. Autoregressive
b. Differencing
c. Moving Average
d. Trend
Answer: c. Moving Averagedatacamp+2
What is required for an ARIMA model to be valid for forecasting?
a. Stationarity of the time series
b. High variance
c. No seasonality
d. Trend in data
Answer: a. Stationarity of the time seriesstat.berkeley+2
Which statistic is commonly used to compare ARIMA model fit?
a. RMSE
b. AIC
c. F-statistic
d. ROC-AUC
Answer: b. AICprojectpro+2
What does ARIMA stand for?
a. Auto Regressive Integrated Moving Average
b. Automated Regression Iterative Model Algorithm
c. Average Regression Interval Model Analysis
d. Adaptive Recurrent Integrated Mean Algorithm
Answer: a. Auto Regressive Integrated Moving Averagescaler+2
The “AR” in ARIMA refers to:
a. The number of moving averages
b. The number of lagged observations
c. The order of seasonal difference
d. The period of the forecast
Answer: b. The number of lagged observationsitfeature+2
The “I” in ARIMA refers to:
a. Independence
b. Integration (differencing)
c. Interpolation
d. Interval
Answer: b. Integration (differencing)cambridge+2
Which function helps identify the value of “p” in ARIMA?
a. ACF
b. PACF
c. RMSE
d. ROC
Answer: b. PACFpeople.stat.sc+2
Which function helps identify the value of “q” in ARIMA?
a. PACF
b. ACF
c. RMSE
d. MAE
Answer: b. ACFapxml+2
In ARIMA, what is the primary role of differencing?
a. Forecast trend
b. Make the series stationary
c. Reduce variance
d. Remove outliers
Answer: b. Make the series stationarydatacamp+2
Which of the following is NOT an assumption of ARIMA?
a. Linearity
b. Nonstationarity
c. Homoscedasticity
d. No autocorrelation in residuals
Answer: b. Nonstationaritystat.berkeley+2
If an ARIMA(0,1,0) is fitted, what is the process called?
a. White noise
b. Random walk
c. Autocorrelation
d. Seasonal trend
Answer: b. Random walkitfeature+2
What does the “p” parameter indicate?
a. Number of differences
b. Number of lags for MA
c. Number of lags for AR
d. Number of seasonal differences
Answer: c. Number of lags for ARscaler+2
The Akaike Information Criterion (AIC) in ARIMA model selection:
a. Penalizes model complexity
b. Rewards overfitting
c. Ignores residual error
d. Is only for seasonal data
Answer: a. Penalizes model complexityprojectpro+2
Which ARIMA model component is responsible for accounting for past errors?
a. AR
b. MA
c. I
d. All of the above
Answer: b. MAdatacamp+2
Which method visually checks time series stationarity?
a. Boxplot
b. Line plot of data
c. ACF/PACF plots
d. Q-Q plot
Answer: b. Line plot of dataucanalytics+2
How is seasonality typically handled in ARIMA?
a. Ignored
b. Through SARIMA/seasonal differencing
c. By increasing “d”
d. By increasing “q”
Answer: b. Through SARIMA/seasonal differencingapxml+2
Which criterion is least useful for ARIMA model selection?
a. AIC
b. BIC
c. Adjusted R-squared
d. Residual diagnostics
Answer: c. Adjusted R-squaredprojectpro+2
If residuals of ARIMA are autocorrelated, what should you do?
a. Model is adequate
b. Re-examine p, d, q
c. Reduce sample size
d. Use a linear model
Answer: b. Re-examine p, d, qscaler+2
What is the main goal of forecasting with ARIMA?
a. Minimize future trend
b. Model past errors
c. Predict future values
d. Maximize lags
Answer: c. Predict future valuesdatacamp+2
In Python, which library frequently implements ARIMA models?
a. pandas
b. scikit-learn
c. statsmodels
d. matplotlib
Answer: c. statsmodelsprojectpro+2
What does over-differencing cause in ARIMA?
a. Seasonality
b. Overfitting and unnecessary noise
c. Better fit
d. No impact
Answer: b. Overfitting and unnecessary noiseapxml+2
If time series after differencing is still nonstationary, you should:
a. Increase p
b. Increase q
c. Increase d
d. Reduce d
Answer: c. Increase dstat.berkeley+2
What kind of data is best suited for ARIMA?
a. Image data
b. Stationary time series data
c. Categorical data
d. Hierarchical data
Answer: b. Stationary time series datastat.berkeley+2
Which measure helps you validate the accuracy of forecasts from ARIMA?
a. ROC
b. AUC
c. RMSE
d. Gain ratio
Answer: c. RMSEdatacamp+2
Box-Jenkins methodology relates to:
a. Identifying ARIMA parameters
b. Plotting time series
c. Clustering
d. Sequential mining
Answer: a. Identifying ARIMA parametersvskills+2
How are the best values for p and q often selected?
a. By random chance
b. By ACF and PACF analysis
c. By plotting scatter plots
d. By maximizing mean values
Answer: b. By ACF and PACF analysispeople.stat.sc+2
The ARIMA(1,1,0) model is equal to:
a. Random walk with drift
b. Simple exponential smoothing
c. Pure AR process
d. White noise
Answer: a. Random walk with driftitfeature+2
Which diagnostic test checks if ARIMA residuals are white noise?
a. Shapiro-Wilk test
b. Ljung-Box test
c. Kruskal-Wallis test
d. Mann-Whitney U test
Answer: b. Ljung-Box teststat.berkeley+2
In an ARMA(1,1) model, p and q are both equal to:
a. 1
b. 0
c. d
d. 2
Answer: a. 1people.stat.sc+2
Which model is NOT encompassed by ARIMA?
a. MA
b. AR
c. SARIMA
d. SVM
Answer: d. SVMitfeature+2
A high value for ACF at lag 1 suggests:
a. High autocorrelation
b. Low prediction
c. High noise
d. Random process
Answer: a. High autocorrelationitfeature+2
What is the main difference between ARIMA and SARIMA?
a. SARIMA models seasonality
b. ARIMA ignores lags
c. ARIMA uses non-time-series data
d. SARIMA introduces overfitting
Answer: a. SARIMA models seasonalitydatacamp+2
The parameter “q” is associated with:
a. Past observations
b. Past forecast errors
c. Seasonality
d. Differencing
Answer: b. Past forecast errorsscaler+2
The ARIMA model’s forecast function predicts:
a. Only next step
b. Only seasonal trends
c. Multiple future points
d. Descriptive statistics
Answer: c. Multiple future pointsprojectpro+2
Which model is a special case of ARIMA?
a. Exponential smoothing
b. Naive
c. Moving average
d. Linear regression
Answer: c. Moving averagescaler+2
In ARIMA, if p is 0 and q is 0, the model is:
a. MA process
b. AR process
c. White noise
d. Linear regression
Answer: c. White noisestat.berkeley+2
The Box-Cox transformation is often used with ARIMA for:
a. Non-linearity
b. Stationarity and variance stabilization
c. Residual analysis
d. Fitting AR term
Answer: b. Stationarity and variance stabilizationapxml+2
The parameter “d” in ARIMA should be:
a. As high as possible
b. Zero for stationary data
c. Always 1
d. Chosen randomly
Answer: b. Zero for stationary datascaler+2
Differencing is used in ARIMA to:
a. Remove autocorrelation
b. Make the mean constant over time
c. Increase variance
d. Lower AIC
Answer: b. Make the mean constant over timeitfeature+2
The partial autocorrelation function (PACF) is typically used to identify:
a. MA order
b. AR order
c. Differencing order
d. Model residuals
Answer: b. AR orderpeople.stat.sc+2
ARIMA models require what kind of time intervals?
a. Equally spaced
b. Random
c. Uneven
d. Variable
Answer: a. Equally spaceddatacamp+2
Higher values of “d” in ARIMA can result in:
a. Underfitting
b. Overdifferencing and loss of information
c. No impact
d. Perfect predictions
Answer: b. Overdifferencing and loss of informationpeople.stat.sc+2
The Q statistic in ARIMA model evaluation tests:
a. Model fit to data
b. Autocorrelation of residuals
c. Nonstationarity
d. Seasonal effect
Answer: b. Autocorrelation of residualsprojectpro+2
Which error metric is NOT routinely used in ARIMA assessment?
a. MSE
b. MAE
c. ROC-AUC
d. RMSE
Answer: c. ROC-AUCstat.berkeley+2
The main advantage of ARIMA over other time series models:
a. Requires no data preparation
b. Handles multiple seasonalities natively
c. Can model different types of time series including those requiring differencing
d. Needs no parameter tuning
Answer: c. Can model different types of time series including those requiring differencingscaler+2
Which best describes an invertible MA(2) process in ARIMA context?
a. Always stationary
b. Nonzero ACF at lags 1 and 2 only
c. Nonzero PACF at k > 2
d. None of the above
Answer: b. Nonzero ACF at lags 1 and 2 onlypeople.stat.sc+2
In the Box-Jenkins methodology, model adequacy checks are done by:
a. Information criterion only
b. Residual diagnostics
c. Only visual inspection
d. Increasing model complexity
Answer: b. Residual diagnosticsdatacamp+2
The prediction interval length for nonstationary ARIMA forecasts:
a. Increases with forecast horizon
b. Decreases with forecast horizon
c. Is constant
d. Is unrelated to horizon
Answer: a. Increases with forecast horizonpeople.stat.sc+2
If ACF and PACF plots both show slow decay, the series is likely:
a. White noise
b. Stationary
c. Nonstationary
d. Overfitted
Answer: c. Nonstationaryitfeature+2

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