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.
Cite
@article{arxiv.2112.05673,
title = {Neural Multi-Quantile Forecasting for Optimal Inventory Management},
author = {Federico Garza Ramírez},
journal= {arXiv preprint arXiv:2112.05673},
year = {2021}
}