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

Using the historical average as the forecast

Answer

Using linear regression over all data

Answer

Using exponential smoothing with α=0.5

Question 2

What is Mean Absolute Percentage Error (MAPE)?

Accepted Answer

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

Answer

The mean of squared errors divided by the mean

Answer

The ratio of MAE to RMSE

Answer

The percentage of forecasts within 5% of actuals

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 is always larger

Answer

They measure the same thing

Answer

Out-of-sample error is computed before training

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

Randomly shuffling time series observations for cross-validation

Answer

Using k-fold cross-validation without regard to time order

Answer

Splitting the data once into 80% train and 20% test

Question 5

What does the Diebold-Mariano test compare?

Accepted Answer

Whether two forecasting models have statistically equal predictive accuracy

Answer

Whether a series is stationary

Answer

Whether ARIMA or ETS fits better based on AIC

Answer

Whether residuals are normally distributed

Question 6

What is the Mean Absolute Scaled Error (MASE)?

Accepted Answer

MAE scaled by the in-sample MAE of the naive forecast, giving a scale-free accuracy metric

Answer

MAE divided by the mean of the series

Answer

Absolute error divided by the standard deviation

Answer

The ratio of RMSE to MAE