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In this work we propose the use of quantile regression and dilated recurrentneural networks with temporal scaling (MQ-DRNN-s) and apply it to the inventorymanagement task. This model showed a better performance of up to 3.2\% over astatistical benchmark (the quantile autoregressive model with exogenousvariables, QAR-X), being better than the MQ-DRNN without temporal scaling by6\%. The above on a set of 10,000 time series of sales of El Globo over a53-week horizon using rolling windows of 7-day ahead each week.

Neural Multi-Quantile Forecasting for Optimal Inventory Management