Approaching sales forecasting using recurrent neural networks and transformers
This work addresses demand forecasting for supply chain management, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled sales forecasting at day/store/item level using deep learning on the Corporación Favorita dataset, achieving a competitive RMSLE of around 0.54 with a simple sequence-to-sequence architecture and minimal preprocessing.
Accurate and fast demand forecast is one of the hot topics in supply chain for enabling the precise execution of the corresponding downstream processes (inbound and outbound planning, inventory placement, network planning, etc). We develop three alternatives to tackle the problem of forecasting the customer sales at day/store/item level using deep learning techniques and the Corporación Favorita data set, published as part of a Kaggle competition. Our empirical results show how good performance can be achieved by using a simple sequence to sequence architecture with minimal data preprocessing effort. Additionally, we describe a training trick for making the model more time independent and hence improving generalization over time. The proposed solution achieves a RMSLE of around 0.54, which is competitive with other more specific solutions to the problem proposed in the Kaggle competition.