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Which forecasts are more accurate?
Forecasts are more accurate for groups or families of items rather than for individual items. When items are grouped together, their individual high and low values can cancel each other out. The data for a group of items can be stable even when individual items in the group are very unstable.
Which measure of forecast accuracy is scale independent?
MAPE: Mean Absolute Percentage Error is the most widely used measure for checking forecast accuracy. It comes under percentage errors which are scale independent and can be used for comparing series on different scales.
Which is the best method for evaluating forecast accuracy?
Here, the best method is the drift method (regardless of which accuracy measure is used). A more sophisticated version of training/test sets is time series cross-validation. In this procedure, there are a series of test sets, each consisting of a single observation.
Which is better an iterated forecast or a direct forecast?
The iterated forecasts typically outperform the direct forecasts, particularly if the models can select long lag specifications. The relative performance of the iterated forecasts improves with the forecast horizon. A free version of their paper is available here: https://www.princeton.edu/~mwatson/papers/hstep_3.pdf
How is the size of a test set used in forecasting?
Because the test data is not used in determining the forecasts, it should provide a reliable indication of how well the model is likely to forecast on new data. The size of the test set is typically about 20% of the total sample, although this value depends on how long the sample is and how far ahead you want to forecast.
Which is more efficient one step ahead or H step ahead?
The direct forecast (when you estimate the model with y t as a function of y t − h in which the ‘one’-step-ahead forecast is now a h -step ahead forecast in ‘physical’ time) is less efficient in this case, but on the upside it is more robust to model misspecification.