Time Series Analysis and Forecasting

Which of the following is NOT a component typically found in time series data?

• Trend
• Seasonality
• Cyclical pattern
• Logarithmic pattern

What is the primary advantage of using the mean absolute percentage error (MAPE) over other error measures?

• It's always positive
• It allows comparisons across different time series
• It gives more weight to recent errors
• It's easier to calculate

In exponential smoothing, what does the smoothing constant α represent?

• The weight given to the most recent observation
• The trend component
• The seasonal factor
• The forecast horizon

Which forecasting method is most appropriate for a time series with both trend and seasonal components?

• Simple moving average
• Single exponential smoothing
• Linear regression with dummy variables
• Naive method

What is the main limitation of using the average of all historical data as a forecast?

• It's too complex to calculate
• It doesn't account for recent changes in the time series
• It always overestimates the forecast
• It can only be used for seasonal data

In a moving average model, what effect does increasing the order k have?

• It makes the forecast more responsive to recent changes
• It smooths out random fluctuations more effectively
• It increases the forecast error
• It eliminates the need for seasonal adjustments

Which of the following is a characteristic of a stationary time series?

• Constant mean over time
• Increasing variance over time
• Clear upward trend
• Cyclical pattern with increasing amplitude

What is the primary difference between time series methods and causal forecasting methods?

• Time series methods are always more accurate
• Causal methods use only past values of the variable being forecast
• Time series methods can't handle seasonal data
• Causal methods incorporate variables believed to influence the forecast variable

In a linear regression model for forecasting, what does the slope coefficient represent?

• The average value of the time series
• The change in y for a one-unit change in x
• The seasonal component
• The forecast error

What is the main advantage of using dummy variables in regression models for forecasting?

• They eliminate all forecast error
• They can capture non-linear relationships
• They can model seasonal effects
• They always improve forecast accuracy