Sample Questions
Question 1
Easy
For a forecast to be useful, the prediction interval should:
Solution
A is correct.
Prediction intervals widen as the forecast horizon increases because uncertainty about future values grows over time. For stationary models (like AR), the interval width converges to a finite limit; for non-stationary models (like random walks), the width grows without bound.
Prediction intervals widen as the forecast horizon increases because uncertainty about future values grows over time. For stationary models (like AR), the interval width converges to a finite limit; for non-stationary models (like random walks), the width grows without bound.
Question 2
Medium
Differencing a time series means computing:
Solution
C is correct.
First differencing computes , the change between consecutive observations. This is commonly used to remove trends and achieve stationarity.
First differencing computes , the change between consecutive observations. This is commonly used to remove trends and achieve stationarity.
Question 3
Hard
Simple exponential smoothing is most appropriate for a time series that:
Solution
A is correct.
Simple exponential smoothing is designed for series with no trend and no seasonality — it produces flat (constant) forecasts. It works best when the data fluctuates around a stable mean level.
Simple exponential smoothing is designed for series with no trend and no seasonality — it produces flat (constant) forecasts. It works best when the data fluctuates around a stable mean level.
