MCQs

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Overall MCQs for Reference

An orderly set of data arranged in accordance with their time of occurrence is called:
a. Arithmetic series
b. Harmonic series
c. Geometric series
d. Time series
Answer: dscribd
Which of these is a key component of a time series?
a. Frequency
b. Fractals
c. Seasonal variation
d. Clusters
Answer: cscribd
A time series consists of:
a. Short-term variations
b. Long-term variations
c. Irregular variations
d. All of the above
Answer: dscribd
What is the primary purpose of moving average in time series?
a. Increase noise
b. Smooth out short-term fluctuations
c. Decrease data
d. Make series nonlinear
Answer: btestbook
The systematic components of time series that follow regular patterns are called:
a. Signal
b. Noise
c. Additive model
d. Multiplicative model
Answer: ascribd
What is the main difference between additive and multiplicative time series models?
a. Number of components
b. Way components combine
c. Use of regression
d. Require equal intervals
Answer: bscribd
The unsystematic sequence which follows irregular pattern of variations is called:
a. Noise
b. Signal
c. Linear
d. Non-linear
Answer: ascribd
Which method uses past values for future time points prediction?
a. Supervised learning
b. Time series forecasting
c. Bagging
d. Clustering
Answer: bigmguru+1
Which model assumes that the value at any time is the sum of various components?
a. Linear regression
b. Additive model
c. Logistic regression
d. Multiplicative model
Answer: bscribd
The formula for the multiplicative time series model is:
a. Y = T + S + C + I
b. Y = TSCI
c. Y = a + bX
d. Y = a + bX + cX^2
Answer: bscribd
What does ARIMA stand for?
a. Average Random Index Moving Analysis
b. AutoRegressive Integrated Moving Average
c. Auto Regression Index Model Application
d. Artificial Regression Integrated Model Algorithm
Answer: bexamveda+1
In time series, which variation appears annually or quarterly?
a. Cyclical
b. Random
c. Seasonal
d. Trend
Answer: cexamveda
Which parameter in ARIMA model accommodates non-stationarity?
a. Auto-regressive
b. Differencing (Integrated)
c. Moving average
d. Significance
Answer: bstudocu
The best-fitted trend line is one where the sum of squares of residuals is:
a. Minimum
b. Maximum
c. Zero
d. One
Answer: ascribd
The method for removing seasonality in time series data is:
a. Differencing
b. Smoothing
c. Histogram
d. Fourier Transform
Answer: astudocu+1
Which of these is NOT a component of a time series?
a. Noise
b. Trend
c. Causality
d. Seasonality
Answer: cscribd
Which algorithm is commonly used for sequence prediction in deep learning?
a. KNN
b. LSTM
c. Decision trees
d. SVM
Answer: bjavaguides+1
In time series decomposition, which component is responsible for up-and-down swings around a trend?
a. Noise
b. Cyclical
c. Seasonality
d. Level
Answer: bscribd
Which metric is preferred for measuring forecast accuracy?
a. RMSE
b. Cross-entropy
c. Purity
d. ROC
Answer: aexamveda
Holt-Winters method is especially used for:
a. Regression
b. Classification
c. Seasonality and trend
d. Clustering
Answer: cstudocu
The term “lag” in time series refers to:
a. Delay in computation
b. Previous value at a time step
c. Highest value
d. Lowest value
Answer: bexamveda+1
If time series data fluctuates without a pattern, it is said to be:
a. Stationary
b. Non-stationary
c. Periodic
d. Uniform
Answer: bexamveda
Which test checks for autocorrelation in time series data?
a. ANOVA
b. Ljung-Box
c. Chi-square
d. Kolmogorov-Smirnov
Answer: bexamveda
What is a rolling window in forecasting?
a. Variable training size
b. Fixed-size sequentially shifting window
c. Randomly selected data
d. Data shuffling
Answer: btestbook
Which neural network is most suited for time series forecasting?
a. CNN
b. LSTM
c. GAN
d. MLP
Answer: bigmguru+1
Differencing is primarily used to:
a. Smooth frequency
b. Remove stationarity
c. Cause non-linearity
d. Achieve stationarity
Answer: dstudocu+1
Which technique helps visualize time series trends?
a. Scatterplot
b. Line chart
c. Pie chart
d. Histogram
Answer: bgyansetu
What is meant by ‘trend’ in time series?
a. Random movement
b. Long-term increase or decrease
c. Sudden dip
d. Annual average
Answer: bscribd
Which component captures unpredictable elements in time series?
a. Trend
b. Seasonal
c. Cyclical
d. Irregular/Noise
Answer: dscribd
Simple exponential smoothing gives more weight to:
a. Future observations
b. Older observations
c. Recent observations
d. Outliers
Answer: cexamveda
Which combination fully expresses additive time series model?
a. Y = T + S + C + I
b. Y = TSCI
c. Y = abX
d. Y = X^2 + Y^2
Answer: ascribd
If the autocorrelation is high, it suggests:
a. Weak dependency
b. Strong dependency
c. Randomness
d. Outliers
Answer: bexamveda
Which method is used to analyze time series with minimum squares error?
a. Least squares method
b. Average method
c. Maximum likelihood
d. Principal components
Answer: aitfeature
A model that predicts based on its own lagged values is called:
a. AR (AutoRegressive)
b. MA (Moving Average)
c. ARIMA
d. GARCH
Answer: astudocu+1
Which factor does NOT influence seasonality in a time series?
a. Weather
b. Holidays
c. Random events
d. Calendar months
Answer: cscribd
When is a time series called “stationary”?
a. When mean and variance are constant over time
b. When periodicity exists
c. When outliers exist
d. When trends are visible
Answer: astudocu+1
Recurrent Neural Networks (RNNs) are used for time series because:
a. They cannot process sequences
b. They handle data of fixed size only
c. They capture dependencies between time steps
d. They use only current input
Answer: cjavaguides+1
An ARMA model is a combination of:
a. AutoRegressive and Moving Average
b. Additive and Multiplicative models
c. Regression and Clustering
d. Classification and Regression
Answer: aexamveda+1
Which process removes trend from the time series?
a. Smoothing
b. Detrending
c. Exponential weighting
d. Interpolation
Answer: bexamveda
Forecast error is calculated as:
a. Actual – Forecasted
b. Forecasted – Trend
c. Actual – Trend
d. Trend + Residual
Answer: ascribd
Which type of neural net can learn very long-term dependencies?
a. LSTM
b. CNN
c. RNN
d. SVM
Answer: aigmguru+1
In time series, the “seasonal index” is used to:
a. Estimate trend
b. Isolate trend component
c. Adjust for seasonality
d. Predict outliers
Answer: cstudocu+1
The autocorrelation function (ACF) measures:
a. Pearson correlation
b. Correlation between lagged values
c. Regression slope
d. Random error
Answer: bexamveda
In ARIMA(p,d,q), ‘q’ is:
a. Auto-regressive order
b. Differencing order
c. Moving average order
d. Non-stationarity order
Answer: cstudocu+1
A set of observations recorded at equal intervals is:
a. Array data
b. Data
c. Geometric series
d. Time series data
Answer: dscribd
If the forecast value and actual value perfectly match, the forecast error is:
a. Zero
b. Minimum
c. Maximum
d. One
Answer: ascribd
What is the purpose of using test/train split in time series modeling?
a. Validate model performance on unseen data
b. Prevent overfitting
c. Evaluate noise
d. Increase training speed
Answer: atestbook+1
Which is NOT true about seasonality?
a. It repeats at a regular interval
b. It is unpredictable
c. It is influenced by calendar effects
d. It can have multiple periods
Answer: bscribd
What is ‘backtesting’ in time series?
a. Re-training the model
b. Evaluating model predictions on historical data
c. Finding correlation
d. Removing data
Answer: bexamveda
Which of these is a popular evaluation metric for time series forecasts?
a. MAE (Mean Absolute Error)
b. F1 score
c. Purity score
d. Sentiment index
Answer: aexamveda

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