Time Series Analysis Forecasting Methods and Evaluation 2

Question 1

What is the 'seasonal naive' forecasting benchmark?

Accepted Answer

Using the value from the same period in the prior season as the forecast

Answer

Seasonal naive forecasts equal the observation from one full seasonal cycle ago, providing a natural baseline for seasonal data.

Question 2

What does Root Mean Square Error (RMSE) penalize more heavily than MAE?

Accepted Answer

Large forecast errors, due to the squaring of residuals

Answer

RMSE squares each error before averaging, making it more sensitive to large deviations than MAE, which treats all errors proportionally.

Question 3

What is a 'prediction interval' in forecasting?

Accepted Answer

A range within which a future observation will fall with a specified probability

Answer

A prediction interval provides bounds (e.g., 95%) within which a single future observation is expected to fall, accounting for both model uncertainty and noise.

Question 4

What causes a forecast to be 'biased'?

Accepted Answer

Systematic over- or under-prediction where the mean error is consistently non-zero

Answer

Bias occurs when a model consistently forecasts too high or too low, resulting in a non-zero mean error (ME) across forecasts.

Question 5

In time series forecasting, what is the 'horizon' h?

Accepted Answer

The number of steps ahead being forecast

Answer

The forecast horizon h specifies how many time periods into the future the forecast extends (e.g., h=12 for 12-step-ahead monthly forecasts).

Time Series Analysis Practice Test

Time Series Analysis Practice Test

Time Series Analysis Forecasting Methods and Evaluation 2