Time Series Analysis Forecasting Methods and Evaluation

Question 1

What is the 'naive' forecasting method?

Accepted Answer

Using the most recent observation as the forecast for all future periods

Answer

The naive method sets every future forecast equal to the last observed value, serving as a simple baseline for comparison.

Question 2

What is Mean Absolute Percentage Error (MAPE)?

Accepted Answer

The average of the absolute percentage differences between forecasts and actual values

Answer

MAPE = mean(|actual - forecast| / |actual|) × 100%, providing a scale-independent accuracy measure expressed as a percentage.

Question 3

What is the key difference between in-sample (training) error and out-of-sample (test) error?

Accepted Answer

In-sample error measures fit on data used for estimation; out-of-sample measures true predictive accuracy on unseen data

Answer

In-sample error can be overly optimistic due to overfitting; only out-of-sample evaluation on a held-out test set truly measures forecast performance.

Question 4

What is 'walk-forward validation' (time series cross-validation)?

Accepted Answer

Sequentially expanding the training window and evaluating forecasts on subsequent observations

Answer

Walk-forward validation respects temporal order by training on data up to time t and forecasting time t+h, then advancing t and repeating.

Question 5

What does the Diebold-Mariano test compare?

Accepted Answer

Whether two forecasting models have statistically equal predictive accuracy

Answer

The Diebold-Mariano test formally tests if the difference in forecast accuracy between two models is statistically significant.

Time Series Analysis Practice Test

Time Series Analysis Practice Test

Time Series Analysis Forecasting Methods and Evaluation