Neural Multi-Quantile Forecasting for Optimal Inventory Management
This work addresses inventory management for businesses, but it is incremental as it builds on existing quantile regression and neural network methods.
The authors tackled the problem of inventory management by proposing a neural multi-quantile forecasting model, which achieved up to 3.2% better performance than a statistical benchmark and 6% improvement over a variant without temporal scaling on a dataset of 10,000 sales time series.
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.