# Approaching sales forecasting using recurrent neural networks and transformers

arXiv: https://arxiv.org/abs/2204.07786 · HTML: https://mlsanity.com/paper/2204.07786/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 16 Apr 2022.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Accurate Uncertainties for Deep Learning Using Calibrated Regression](https://mlsanity.com/paper/1807.00263/) (2018)
- [Professor Forcing: A New Algorithm for Training Recurrent Networks](https://mlsanity.com/paper/1610.09038/) (2016)
- [Calibrated Model-Based Deep Reinforcement Learning](https://mlsanity.com/paper/1906.08312/) (2019)
- [Learning Models over Relational Data using Sparse Tensors and Functional Dependencies](https://mlsanity.com/paper/1703.04780/) (2017)
- [A Layered Aggregate Engine for Analytics Workloads](https://mlsanity.com/paper/1906.08687/) (2019)
- [Rk-means: Fast Clustering for Relational Data](https://mlsanity.com/paper/1910.04939/) (2019)
- [Sales forecasting using WaveNet within the framework of the Kaggle competition](https://mlsanity.com/paper/1803.04037/) (2018)
- [Multi-layer Optimizations for End-to-End Data Analytics](https://mlsanity.com/paper/2001.03541/) (2020)
- [On the Impact of Deep Learning-based Time-series Forecasts on Multistage Stochastic Programming Policies](https://mlsanity.com/paper/2009.00665/) (2020)

## Built on by (most cited first, 3 of 3)

- [Large Scale Foundation Models for Intelligent Manufacturing Applications: A Survey](https://mlsanity.com/paper/2312.06718/) (2023)
- [A Primer on Temporal Graph Learning](https://mlsanity.com/paper/2401.03988/) (2024)
- [Temporal Fusion Transformer for Multi-Horizon Probabilistic Forecasting of Weekly Retail Sales](https://mlsanity.com/paper/2511.00552/) (2025)

## Abstract (arXiv)

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 Corporaci'on 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.