MCQs

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Overall MCQs for Reference

Which of the following best defines a time series?
a. Data collected at random intervals
b. Data collected over a sequence of time periods
c. Data with no temporal order
d. Data collected only once
Answer: b. Data collected over a sequence of time periodsstudocu+1
The main objective of time series analysis is:
a. Randomizing values
b. Predicting future values
c. Reducing dimensionality
d. Minimizing variables
Answer: b. Predicting future valuescevnews+1
Which component is not part of classical time series decomposition?
a. Trend
b. Seasonal
c. Interpolation
d. Irregular
Answer: c. Interpolationitfeature+1
What is trend in a time series?
a. Short-term fluctuation
b. Long-term increase or decrease
c. Repeating seasonal pattern
d. Random noise
Answer: b. Long-term increase or decreaseitfeature+1
Which method is commonly used to remove seasonality from time series data?
a. Least squares
b. Differencing
c. Normalization
d. Multiplication
Answer: b. Differencingtowardsdatascience+1
What does “autocorrelation” measure in time series analysis?
a. Correlation between two distinct datasets
b. Similarity between current and lagged values
c. Frequency of data points
d. Outliers
Answer: b. Similarity between current and lagged valuesgeeksforgeeks+1
What is the primary advantage of using AI models, such as LSTM, for time series forecasting?
a. Handles non-sequential data
b. Captures long-term dependencies
c. Reduces data size
d. Increases randomness
Answer: b. Captures long-term dependenciesdatacamp+1
Which model does not focus on time-dependent structure?
a. ARIMA
b. LSTM
c. SVM
d. Exponential Smoothing
Answer: c. SVMdatabricks+1
In time series analysis, what is “seasonality”?
a. One-time event
b. Short-term noise
c. Regular, periodic fluctuations
d. Sudden outburst
Answer: c. Regular, periodic fluctuationstigerdata+1
Which is a graphical method to visualize time series data?
a. Histograms
b. Line plots
c. Box plots
d. Pie charts
Answer: b. Line plotsgyansetu+1
The residual or noise component in time series is:
a. Systematic pattern
b. Predictable
c. Irregular and random
d. Seasonal effect
Answer: c. Irregular and randomstudocu+1
What is the additive time series model formula?
a. Y = T × S × C × I
b. Y = T + S + C + I
c. Y = (T + S) × C × I
d. Y = (T + I) × S × C
Answer: b. Y = T + S + C + Istudocu+1
Which deep learning architecture is widely used for sequence and time series data?
a. CNN
b. RNN
c. GAN
d. k-NN
Answer: b. RNNdatacamp+1
A stationary time series has:
a. Changing mean and variance
b. Constant mean and variance
c. Increasing trend
d. Seasonal spikes only
Answer: b. Constant mean and variancegeeksforgeeks+1
What is the main feature of ARIMA models?
a. Combines autoregression and moving average
b. Only analyzes seasonality
c. Does not allow trend
d. Uses clustering
Answer: a. Combines autoregression and moving averagegeeksforgeeks+1
Smoothing techniques in time series are mainly used to:
a. Increase variability
b. Highlight outliers
c. Reduce random variation
d. Erase trend
Answer: c. Reduce random variationgeeksforgeeks+1
The cyclic component of time series refers to:
a. Regular yearly repetition
b. Fluctuations with no fixed period
c. Purely random changes
d. Data errors
Answer: b. Fluctuations with no fixed periodglobal.oup+1
In time series, “lag” refers to:
a. Skipped values
b. Time interval between observations
c. Correlation between unrelated sequences
d. Forecast error
Answer: b. Time interval between observationsitfeature+1
Outliers in time series can be detected using:
a. Simple plotting only
b. Residual analysis
c. Ignoring trends
d. Removing all data
Answer: b. Residual analysistowardsdatascience+1
Which model accounts for seasonality explicitly?
a. ARIMA
b. SARIMA
c. k-NN
d. SVM
Answer: b. SARIMAdatabricks+1
What is “differencing” used for in time series analysis?
a. Transforming data to stationary
b. Smoothing data
c. Randomizing sequence
d. Reducing size
Answer: a. Transforming data to stationarytowardsdatascience+1
Which is not a component of time series?
a. Noise
b. Trend
c. Median
d. Seasonality
Answer: c. Mediantigerdata+1
What is the usual first step in time series analysis?
a. Collecting random samples
b. Data visualization
c. Applying deep learning
d. Feature scaling
Answer: b. Data visualizationdatacamp+1
The model Y = T × S × C × I in time series is called:
a. Additive
b. Subtractive
c. Multiplicative
d. Hybrid
Answer: c. Multiplicativestudocu+1
Which AI model uses self-attention mechanisms for time series?
a. RNN
b. LSTM
c. Transformer
d. CNN
Answer: c. Transformerdatabricks+1
What is a “rolling mean” in time series analysis?
a. Sum of all values
b. Moving average
c. Product of previous values
d. Constant value
Answer: b. Moving averagegeeksforgeeks+1
Which method would best forecast future values while considering multiple exogenous variables?
a. Simple exponential smoothing
b. Prophet
c. Linear regression
d. ARIMAX
Answer: d. ARIMAXgeeksforgeeks+1
Stationarity in time series is important because:
a. Non-stationary data is easier to model
b. Most models assume stationarity
c. Prevents AI use
d. Guarantees randomness
Answer: b. Most models assume stationaritytigerdata+1
In time series, “window size” is a parameter for:
a. ARIMA order
b. Neural network layers
c. Moving average smoothing
d. Seasonal adjustment
Answer: c. Moving average smoothinggeeksforgeeks+1
A “spike” in time series refers to:
a. Extended trend
b. Short, sudden increase
c. Long-term cycle
d. Subtle seasonality
Answer: b. Short, sudden increaseitfeature+1
Which time series AI method is most robust to missing data?
a. LSTM
b. ARIMA
c. Decision Trees
d. Linear Regression
Answer: a. LSTMdatacamp+1
What is the key advantage of using convolutional neural networks (CNNs) for time series?
a. Only for images
b. Extracts features and local patterns
c. Increases randomness
d. Flattens sequences
Answer: b. Extracts features and local patternsdatacamp
The objective of “decomposition” in time series is to:
a. Merge multiple datasets
b. Separate components like trend and seasonality
c. Only plot data
d. Remove forecasts
Answer: b. Separate components like trend and seasonalityitfeature+1
Which is an example of a stationary process?
a. Time series with changing mean
b. White noise
c. Exponential trend
d. Non-periodic cycles
Answer: b. White noisetigerdata+1
The “forecast horizon” in time series forecasting refers to:
a. Size of training data
b. Length of time into the future predictions are made
c. Last value in series
d. Starting observation
Answer: b. Length of time into the future predictions are madedatabricks+1
Which is a key step before fitting any time series model?
a. Data randomization
b. Data pre-processing
c. Ignoring missing values
d. Removing trend
Answer: b. Data pre-processingtigerdata+1
What technique can address non-linearity in time series data for AI models?
a. Linear regression
b. Feature engineering
c. Random guessing
d. Spurious correlation
Answer: b. Feature engineeringtowardsdatascience+1
“Overfitting” in time series AI models can be reduced by:
a. Using all available features
b. Cross-validation
c. Ignoring test data
d. Increasing noise
Answer: b. Cross-validationdatabricks+1
Which is a direct application of time series analysis in AI?
a. Handwriting recognition
b. Forecasting stock prices
c. Speech-to-text
d. k-means clustering
Answer: b. Forecasting stock pricesdatacamp+1
What is exponential smoothing used for?
a. Increasing peaks
b. Smoothing short-term fluctuations
c. Removing trend entirely
d. Random forest modeling
Answer: b. Smoothing short-term fluctuationsgeeksforgeeks+1
The term “lead” in time series analysis often means:
a. Previous value
b. Next future value
c. Minimum value
d. Outlier
Answer: b. Next future valuegeeksforgeeks+1
Which ensemble AI approach is commonly used for time series forecasting?
a. Bagging
b. Boosting
c. Random forest
d. All of the above
Answer: d. All of the abovedatabricks
What is backtesting in time series modeling?
a. Forecasting only past values
b. Evaluating model performance on historical data
c. Ignoring test set
d. Fitting new models
Answer: b. Evaluating model performance on historical datatigerdata+1
In AI, which layer helps RNNs remember long-term dependencies in time series?
a. Output layer
b. LSTM layer
c. Dense layer
d. Embedding layer
Answer: b. LSTM layerdatacamp+1
What does ACF stand for in time series?
a. Approximate Change Factor
b. Autocorrelation Function
c. Average Cumulative Forecast
d. Automated Control Factor
Answer: b. Autocorrelation Functionitfeature+1
Which metric is commonly used to evaluate forecasting accuracy?
a. Precision
b. ROC-AUC
c. Mean Absolute Error (MAE)
d. Mutual information
Answer: c. Mean Absolute Error (MAE)tigerdata+1
In time series, “seasonal adjustment” is:
a. Removing all trend
b. Accounting for regular periodic effects
c. Increasing model error
d. Reducing data size
Answer: b. Accounting for regular periodic effectsgeeksforgeeks+1
What does partial autocorrelation tell in time series?
a. Total variance
b. Correlation at a specific lag, after removing previous lags
c. Cumulative prediction
d. Lead values
Answer: b. Correlation at a specific lag, after removing previous lagsitfeature+1
The “window method” in time series AI splits data by:
a. Random shuffling
b. Continuous, overlapping sub-sequences
c. Removing data
d. Forecasting each point independently
Answer: b. Continuous, overlapping sub-sequencesdatabricks
What does differencing order ‘d’ in ARIMA represent?
a. Number of terms in trend
b. Number of times data differenced for stationarity
c. Total seasonal cycles
d. Irregular variation
Answer: b. Number of times data differenced for stationarityitfeature+1

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