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arXiv · 2112.05673

Neural Multi-Quantile Forecasting for Optimal Inventory Management

Federico Garza Ramírez
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Abstract

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.

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BibTeXRIS

Federico Garza Ramírez. 2021-12-10. Neural Multi-Quantile Forecasting for Optimal Inventory Management. https://arxiv.org/abs/2112.05673

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