Q.1 Which of the following best defines a time series?
A collection of unrelated observations
Data collected over a period of time at regular intervals
A random sample of data points
Data grouped by categories
Explanation - A time series consists of observations recorded sequentially over time, typically at regular intervals.
Correct answer is: Data collected over a period of time at regular intervals
Q.2 Which component of time series captures long-term movement of data?
Seasonal
Cyclical
Trend
Irregular
Explanation - The trend component represents the long-term progression in the data, showing increase, decrease, or stagnation.
Correct answer is: Trend
Q.3 Which time series component shows patterns repeating within a fixed period, like months or quarters?
Trend
Cyclical
Seasonal
Irregular
Explanation - Seasonal variations repeat at regular intervals due to seasonal factors like weather, festivals, or business cycles.
Correct answer is: Seasonal
Q.4 Cyclical variations in time series are different from seasonal variations because they:
Occur randomly
Occur over periods longer than a year
Repeat every month
Are part of irregular fluctuations
Explanation - Cyclical variations are long-term oscillations often tied to economic or business cycles, unlike seasonal variations which recur annually.
Correct answer is: Occur over periods longer than a year
Q.5 Which method is suitable for smoothing time series data to identify the underlying trend?
Linear regression
Moving average
Correlation coefficient
Hypothesis testing
Explanation - The moving average method smooths out short-term fluctuations and highlights the long-term trend in time series data.
Correct answer is: Moving average
Q.6 A 12-month moving average is typically used to remove which component in monthly data?
Trend
Seasonal
Cyclical
Irregular
Explanation - A 12-month moving average smooths out seasonal effects in monthly data to better show trend and cyclical components.
Correct answer is: Seasonal
Q.7 Which of the following is NOT a purpose of time series analysis?
Forecasting future values
Identifying patterns in data
Testing causality between variables
Understanding past fluctuations
Explanation - Time series analysis primarily focuses on identifying patterns and forecasting, not on testing causal relationships.
Correct answer is: Testing causality between variables
Q.8 What type of model expresses a time series as a sum of components: trend, seasonal, and irregular?
Multiplicative model
Additive model
Exponential model
Linear regression model
Explanation - In the additive model, the observed value is represented as the sum of trend, seasonal, and irregular components.
Correct answer is: Additive model
Q.9 In a multiplicative time series model, the observed value is represented as:
Sum of components
Product of components
Difference of components
Ratio of components
Explanation - The multiplicative model expresses the time series as a product of trend, seasonal, and irregular components, suitable when seasonal variations increase with the level of the series.
Correct answer is: Product of components
Q.10 Which method is commonly used for forecasting when trend and seasonal components are present?
Simple exponential smoothing
Holt-Winters method
Linear regression
Moving median
Explanation - Holt-Winters exponential smoothing accounts for level, trend, and seasonality, making it suitable for forecasting such time series.
Correct answer is: Holt-Winters method
Q.11 Which smoothing technique gives more weight to recent observations in a time series?
Simple moving average
Weighted moving average
Simple exponential smoothing
Linear regression
Explanation - Exponential smoothing assigns exponentially decreasing weights to past observations, emphasizing more recent data.
Correct answer is: Simple exponential smoothing
Q.12 Deseasonalizing a time series involves:
Removing trend component
Removing seasonal component
Adding irregular component
Multiplying all values by a constant
Explanation - Deseasonalizing isolates trend and cyclical patterns by removing recurring seasonal effects.
Correct answer is: Removing seasonal component
Q.13 If a series has upward trend and multiplicative seasonality, which method is preferred?
Additive decomposition
Multiplicative decomposition
Simple moving average
Linear regression
Explanation - Multiplicative decomposition is appropriate when seasonal variations change proportionally with the level of the series.
Correct answer is: Multiplicative decomposition
Q.14 Which plot is most useful to visualize seasonal patterns in time series?
Histogram
Scatter plot
Line plot
Box plot
Explanation - Line plots show data points in chronological order, making it easier to observe seasonality and trends.
Correct answer is: Line plot
Q.15 Autocorrelation in a time series measures:
Correlation between trend and seasonality
Correlation between observations at different times
Correlation between two different series
Random noise
Explanation - Autocorrelation checks whether past values of a series are correlated with future values at specific lags.
Correct answer is: Correlation between observations at different times
Q.16 Which of the following is a limitation of moving average method?
Cannot smooth data
Does not forecast future accurately in presence of trend
Requires large data sets
Does not show seasonal patterns
Explanation - Simple moving average smooths data but does not adjust for trends or seasonality, limiting forecasting accuracy.
Correct answer is: Does not forecast future accurately in presence of trend
Q.17 Index numbers are often used in time series to:
Predict future trends
Measure relative changes over time
Identify irregular components
Calculate moving averages
Explanation - Index numbers express values relative to a base period, helping analyze time-related changes in data.
Correct answer is: Measure relative changes over time
Q.18 Which component of time series is unpredictable and random?
Trend
Cyclical
Seasonal
Irregular
Explanation - The irregular component includes random fluctuations caused by unforeseen or accidental events.
Correct answer is: Irregular
Q.19 Which method separates a time series into trend, seasonal, and irregular components?
Decomposition method
Correlation analysis
Regression analysis
Autocorrelation
Explanation - Decomposition splits a time series into constituent components to study patterns and aid forecasting.
Correct answer is: Decomposition method
Q.20 In a seasonal index, a value greater than 100 indicates:
Below-average activity
Average activity
Above-average activity
No activity
Explanation - Seasonal index values >100 indicate that the variable's value is above the average for that season.
Correct answer is: Above-average activity
Q.21 Which of these is a non-parametric method in time series smoothing?
Moving average
Exponential smoothing
Holt-Winters
ARIMA
Explanation - Moving average is a non-parametric method as it does not assume a specific functional form of the underlying series.
Correct answer is: Moving average
Q.22 Forecasting using naive method implies:
Using last period's actual value as forecast
Using average of past periods
Using moving average
Using regression model
Explanation - Naive forecasting assumes the most recent observed value is the best estimate for the next period.
Correct answer is: Using last period's actual value as forecast
Q.23 Which approach is used to forecast with ARIMA models?
Decomposition of series
Autoregressive and moving average terms
Simple exponential smoothing
Multiplicative seasonal adjustment
Explanation - ARIMA models use autoregressive (AR), integrated (I), and moving average (MA) components to model time series data for forecasting.
Correct answer is: Autoregressive and moving average terms
Q.24 Time series data can be:
Cross-sectional only
Longitudinal only
Both cross-sectional and longitudinal
Neither cross-sectional nor longitudinal
Explanation - Time series data tracks the same variable(s) over time, which is a form of longitudinal data.
Correct answer is: Longitudinal only
Q.25 Which statistical measure helps in detecting seasonality?
Autocorrelation function (ACF)
Mean
Standard deviation
Skewness
Explanation - ACF measures correlation between observations at different lags, helping detect repeating seasonal patterns.
Correct answer is: Autocorrelation function (ACF)