WorksheetsGSDSA Quiz on TIme Series
Total questions: 50
Worksheet time: 38mins
Time series data consist of observations collected at:
Random intervals
Successive, consistent intervals
One-time events
Unordered sequences
The key characteristic that distinguishes time series data from cross-sectional data is:
Accuracy
Temporal dependency
Sample size
Data source
A smartwatch recording sleep hours daily is an example of:
Predictive data
Time series data
Cross-sectional data
Big data
Time series analysis is primarily used to:
Describe past events only
Predict future events based on past patterns
Identify correlations only
Compare unrelated datasets
Which component represents long-term direction in the data?
Seasonality
Trend
Noise
Cycle
A fixed, repeating pattern such as yearly or monthly fluctuations refers to:
Trend
Randomness
Seasonality
Variation
Irregular or unpredictable movements in a time series are called:
Trend
Seasonality
Cyclical variation
Noise
Cyclical patterns differ from seasonal patterns because they:
Repeat regularly
Occur randomly
Have no fixed period
Occur monthly
Which of the following is NOT a core component of time series?
Trend
Seasonality
Cycles
Regression
The assumption that statistical properties remain constant over time defines:
Stability
Stationarity
Normality
Variability
A line chart is ideal for showing:
Category comparisons
Time-based data
Proportions
Frequency distributions
A bar chart is best for:
Tracking trends over time
Comparing categories
Highlighting outliers
Displaying cumulative totals
A boxplot shows:
Changes across time
Relationship between variables
Distribution, spread, and outliers
Seasonality patterns
The middle line in a boxplot represents the:
Mean
Mode
Median
Minimum
In an area chart, the shaded region helps emphasize:
Interquartile range
Volume or magnitude
Accuracy
Data quality
Which component of the boxplot spans from Q1 to Q3?
Whiskers
Median line
Interquartile range
Data bounds
Line charts are preferred over bar charts for time series because they:
Use color better
Emphasize continuous trends
Show more categories
Are easier to compute
A bar chart with two bars per year comparing subjects is an example of:
Univariate time series
Multivariate comparison
Panel regression
Seasonal modeling
A chart showing visitor counts over months is likely a:
Box plot
Histogram
Line or area chart
Pie chart
Outliers in a boxplot are represented by:
Bars
Dots beyond whiskers
Bold lines
Shaded areas
Simple Moving Average (SMA) is used to:
Highlight seasonal peaks
Smooth short-term fluctuations
Identify outliers
Estimate medians
Exponential smoothing gives more weight to:
Older data
Recent data
Median values
Maximum values
AR (Autoregressive) models use:
External variables
Past values of the series
Noise patterns
Rolling averages
MA (Moving Average) models use:
Past errors in forecasting
Cyclical variations
Median smoothing
Seasonal indices
ARIMA stands for:
Advanced Regression in Multiple Analysis
Autoregressive Integrated Moving Average
Automated Ratio Integrated Model Algorithm
Autocorrelation Repeated Matrix Analysis
ARIMA is suitable for:
Stationary data only
Raw non-stationary data
Time series with no patterns
Continuous cross-sectional data
Differencing is used to achieve:
Seasonality
Stationarity
Random variation
Trend amplification
Holt-Winters exponential smoothing is best for data with:
Trend only
Seasonality only
Both trend and seasonality
No structural pattern
A model that uses many variables to forecast is called:
Univariate
Multivariate
Semi-structural
Random
Forecasting aims to estimate:
Present conditions
Past behavior
Future values beyond the current data
Unrelated variables
Prediction differs from forecasting because prediction:
Uses future data
Estimates values within known data
Is more accurate
Uses machine learning
LSTM networks (mentioned in the summary) are designed for:
Big data storage
Linear regressions
Complex, nonlinear time series
Cross-sectional comparison
A stationary series has:
Constant mean and variance
Increasing variance
Oscillating patterns
Missing seasonality
A component that repeats every year is considered:
Cyclical
Irregular
Seasonal
Noise
Cycles are often related to:
Random noise
High-frequency seasonality
Economic conditions
Missing values
In the module’s library visitor activity, the X-axis represents:
Visitor count
Time (Months)
Student categories
Forecast values
The Y-axis in the same activity represents:
Month names
Average daily visitors
Percentage growth
Variance
Plotting points and connecting them creates a:
Histogram
Pie chart
Time series line chart
Scatterplot
Univariate time series involve:
Many variables
One variable over time
Two categories per year
Forecast errors only
Multivariate time series involve:
Multiple variables over time
A single variable
Stationary-only data
No seasonal patterns
If data shows clear trend and seasonality, the most appropriate model is:
ARIMA (no seasonal component)
Linear regression
Holt-Winters exponential smoothing
Boxplot modeling
The purpose of monitoring and adjusting forecasts is to:
Change data sources
Improve accuracy over time
Remove seasonal factors
Delete old observations
A sudden spike due to a university event is an example of:
Trend
Seasonality
Irregular variation
Cycle
Consistent peaks every March in library use indicate:
Noise
Trend
Seasonal pattern
Irregularity
The Practical Activity requires how many months of data?
10
12
15
20
The ideal chart type for time-based data in the activity is a:
Bar chart
Line chart
Boxplot
Pie chart
The model-selection task asks learners to justify their choice based on:
Data size
Trend and seasonality components
User preference
Chart color
The module suggests that the best forecasting model is:
Always the most complex
The one that fits data behavior and goals
The simplest one
One with the highest computation
Time series analysis is emphasized as both a science and:
Guesswork
Art requiring intuition
Purely mechanical task
Simple calculation
Understanding real-world context is important because:
Data always predicts perfectly
External factors often affect patterns
Patterns never change
Seasonality explains everything
