  In this work we propose the use of quantile regression and dilated recurrent
neural networks with temporal scaling (MQ-DRNN-s) and apply it to the inventory
management task. This model showed a better performance of up to 3.2\% over a
statistical benchmark (the quantile autoregressive model with exogenous
variables, QAR-X), being better than the MQ-DRNN without temporal scaling by
6\%. The above on a set of 10,000 time series of sales of El Globo over a
53-week horizon using rolling windows of 7-day ahead each week.
