  Most forecasting methods use recent past observations (lags) to model the
future values of univariate time series. Selecting an adequate number of lags
is important for training accurate forecasting models. Several approaches and
heuristics have been devised to solve this task. However, there is no consensus
about what the best approach is. Besides, lag selection procedures have been
developed based on local models and classical forecasting techniques such as
ARIMA. We bridge this gap in the literature by carrying out an extensive
empirical analysis of different lag selection methods. We focus on deep
learning methods trained in a global approach, i.e., on datasets comprising
multiple univariate time series. The experiments were carried out using three
benchmark databases that contain a total of 2411 univariate time series. The
results indicate that the lag size is a relevant parameter for accurate
forecasts. In particular, excessively small or excessively large lag sizes have
a considerable negative impact on forecasting performance. Cross-validation
approaches show the best performance for lag selection, but this performance is
comparable with simple heuristics.
