MCQs

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Overall MCQs for Reference

Which of the following best defines a time series?
a. Random numeric collection
b. Sequence of data points, measured at successive times
c. Set of unrelated observations
d. List of sorted numbers
Answer: bserokell+2
What is a primary reason for analyzing time series data in machine learning?
a. To predict random outcomes
b. To forecast future values based on patterns
c. For static model development only
d. To count data points
Answer: bibm+2
Which component is NOT a fundamental part of time series decomposition?
a. Trend
b. Seasonality
c. Clustering
d. Residual/Error
Answer: cgeeksforgeeks+2
What is stationarity in time series?
a. Data distribution changes over time
b. Statistical properties remain constant over time
c. Increasing variance over time
d. Series length is fixed
Answer: bdevinterview+2
Which method is commonly used to test stationarity?
a. Chi-squared test
b. Augmented Dickey-Fuller (ADF) test
c. ROC Curve
d. Confusion Matrix
Answer: bprojectpro+2
Which machine learning model is most specialized for sequential time-based data modeling?
a. Decision Tree
b. SVM
c. LSTM
d. Naïve Bayes
Answer: cmathworks+2
What does ARIMA stand for in time series modeling?
a. Auto Random Integrated Model Association
b. Autoregressive Integrated Moving Average
c. Automatic Regression Interval Median Analysis
d. Augmented Regression Iterative Model Algorithm
Answer: bserokell+2
In ARIMA, which parameter typically handles trend removal?
a. Autoregressive order (p)
b. Differencing order (d)
c. Moving average order (q)
d. Seasonal order (s)
Answer: bprojectpro+2
What type of plot is used to check autocorrelation in a time series?
a. Bar chart
b. Scatter plot
c. Autocorrelation Function (ACF) plot
d. Pie chart
Answer: cgithub+2
What is the Moving Average (MA) model in time series?
a. Uses average of all values
b. Uses a linear combination of previous errors
c. Ignores time order
d. Measures central tendency
Answer: bgithub+2
Which characteristic distinguishes time series from cross-sectional data?
a. High dimensionality
b. Time dependency
c. Random order
d. Missing values
Answer: bdevinterview+2
What is the key advantage of LSTM over standard RNNs for time series?
a. Simpler architecture
b. Adaptive kernel size
c. Ability to remember long-term dependencies
d. High overfitting risk
Answer: cinterviewkickstart+2
What is a lag feature in time series modeling?
a. Next step value
b. Value at a previous time step
c. Averaged values
d. Constant value
Answer: bkaggle+2
Which metric is NOT typically used for evaluating time series forecasts?
a. Mean Squared Error (MSE)
b. Root Mean Squared Error (RMSE)
c. F1-score
d. Mean Absolute Error (MAE)
Answer: cgeeksforgeeks+2
How does differencing help in time series analysis?
a. Reduces data dimension
b. Removes trend and makes data stationary
c. Imputes missing values
d. Randomizes data
Answer: bserokell+2
What type of transformation is commonly applied to time series data to stabilize variance?
a. Log transformation
b. Detrending
c. Min-max scaling
d. Inverse scaling
Answer: aitfeature+2
Which forecasting approach uses past observed values only?
a. Naive Forecast
b. Regression
c. Clustering
d. Neural network
Answer: agithub+2
What is seasonality in a time series?
a. Random fluctuation
b. Regular periodic variation
c. Structural break
d. Trend reversal
Answer: bibm+2
Which of the following models can capture seasonality explicitly?
a. Linear regression
b. Naive Bayes
c. SARIMA
d. KNN
Answer: cprojectpro+2
What is the purpose of backtesting in time series modeling?
a. Increase sample size artificially
b. Evaluate model out-of-sample performance
c. Train deeper models
d. Reduce features
Answer: bdevinterview+2
Which deep learning model helps detect local patterns in time series data?
a. CNN
b. PCA
c. K-Means
d. Linear Regression
Answer: amathworks+2
What is the main role of feature engineering in time series tasks?
a. Model selection
b. Extract meaningful features to improve predictions
c. Normalize data only
d. Regularize model
Answer: bkaggle+2
What would indicate a time series is non-stationary?
a. Constant mean and variance
b. Changing mean or variance over time
c. No trend
d. No seasonality
Answer: bserokell+2
Which method is used for imputing missing data in a time series?
a. Drop all missing
b. Forward fill
c. Random noise addition
d. Irreversible
Answer: bprojectpro+2
Which ACF plot characteristic signals seasonality in the data?
a. Peaks at regular intervals
b. No visible pattern
c. Constant high values
d. Random spikes
Answer: agithub+2
What is the term for sudden, unpredictable deviations in a time series?
a. Residuals
b. Outliers
c. Seasonality
d. Stationarity
Answer: bgeeksforgeeks+2
In time series, what does the “window” in moving average refer to?
a. All data
b. Subset of consecutive data points
c. Future data
d. Random sample
Answer: bibm+2
Which method assesses the importance of past observations when forecasting?
a. Lag analysis
b. Correlation matrix
c. Randomization
d. P-value checking
Answer: adevinterview+2
What does the “autoregressive” part of ARIMA refer to?
a. Regression on external variables
b. Regression on previous values of the series
c. Moving average on noise
d. Detrending
Answer: bserokell+2
What is the main difference between supervised and unsupervised time series tasks?
a. Availability of labeled target values
b. Data type
c. Data length
d. Feature scaling
Answer: aprojectpro+2
The “white noise” in a time series model most closely means:
a. Predictable sequence
b. Purely random sequence
c. High-frequency trend
d. Outlier sequence
Answer: bibm+2
Which kind of neural network is typically used for long-term dependencies in sequential data?
a. Shallow NN
b. CNN
c. LSTM
d. SVM
Answer: cinterviewkickstart+2
Which validation technique is best for time series?
a. Random train-test split
b. k-fold cross-validation
c. Time-based split or rolling-window
d. Leave-one-out
Answer: cgithub+2
What is the main assumption of ARMA models?
a. Non-stationary series
b. Stationary series
c. Multiclass labels
d. Supervised targets
Answer: bdevinterview+2
What does the term “forecast horizon” refer to in time series prediction?
a. Feature depth
b. Number of steps ahead to predict
c. Training epochs
d. Outlier detection
Answer: bserokell+2
In multivariate time series, what differs from univariate?
a. Uses a single variable
b. Incorporates multiple, related time-dependent variables
c. Lacks time indices
d. Static data
Answer: bprojectpro+2
Which model is NOT typically suited for time series?
a. ARIMA
b. SVM
c. Prophet
d. Exponential Smoothing
Answer: bibm+2
What primary property does differencing test in time series?
a. Seasonality
b. Stationarity
c. Outliers
d. Serial correlation
Answer: bdevinterview+2
Which is a suitable loss function for time series regression tasks?
a. Categorical Crossentropy
b. Focal Loss
c. Mean Squared Error (MSE)
d. AUC
Answer: cgeeksforgeeks+2
The “trend” component in time series data is:
a. Short-lived cyclical pattern
b. Long-term increase or decrease
c. Seasonal random movements
d. Outlier driven
Answer: bgeeksforgeeks+2
What is a common cause of overfitting in time series models?
a. Too much noise
b. Too long training window
c. Using future information in training
d. Ignoring lag
Answer: cserokell+2
What is lag-1 autocorrelation?
a. Correlation between current value and one prior value
b. Correlation with the mean
c. Unrelated to sequence
d. Replacing missing values
Answer: agithub+2
Which model is known for being robust to missing data in time series?
a. Prophet
b. Linear Regression
c. Random Forest
d. AR
Answer: aibm+2
In LSTM, which cell is responsible for retaining long-term memory?
a. Output cell
b. Input gate
c. Forget gate
d. Memory cell
Answer: dmathworks+2
Which transformation helps stabilize both mean and variance?
a. Log transformation
b. Power transformation
c. Square root transformation
d. All of the above
Answer: ditfeature+2
Which type of neural network architecture is best for modeling spatial-temporal dependencies?
a. CNN-LSTM hybrid
b. SVM
c. Naive Bayes
d. Logistic regression
Answer: amathworks+2
Which phenomenon is problematic when a model fits noise rather than the signal?
a. Underfitting
b. Overfitting
c. Seasonality
d. Stationarity
Answer: bprojectpro+2
What role does ‘baseline model’ play in time series analysis?
a. Provides minimum standard for performance
b. Only helps in visualization
c. Used for clustering only
d. Is the same as the final model
Answer: agithub+2
Which method is best suited for forecasting in the presence of trend and seasonality?
a. Simple Mean
b. ARIMA
c. SARIMA
d. Random Forest
Answer: cdevinterview+2
Which tool is widely adopted for time series visualization and exploratory analysis?
a. Pandas
b. Matplotlib
c. Excel
d. All of the above
Answer: dkaggle+2

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