# Sales forecasting using WaveNet within the framework of the Kaggle competition

arXiv: https://arxiv.org/abs/1803.04037 · HTML: https://mlsanity.com/paper/1803.04037/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2018.

## Built on by (most cited first, 4 of 4)

- [Parameter Efficient Deep Probabilistic Forecasting](https://mlsanity.com/paper/2112.02905/) (2021)
- [Short term prediction of demand for ride hailing services: A deep learning approach](https://mlsanity.com/paper/2212.03956/) (2022)
- [Approaching sales forecasting using recurrent neural networks and transformers](https://mlsanity.com/paper/2204.07786/) (2022)
- [How Faithful Is Attribution for Sales Forecasting? A Counterfactual Study](https://mlsanity.com/paper/2609.04797/) (2026)

## Abstract (arXiv)

We took part in the Corporacion Favorita Grocery Sales Forecasting competition hosted on Kaggle and achieved the 2nd place. In this abstract paper, we present an overall analysis and solution to the underlying machine-learning problem based on time series data, where major challenges are identified and corresponding preliminary methods are proposed. Our approach is based on the adaptation of dilated convolutional neural network for time series forecasting. By applying this technique iteratively to batches of n examples, a big amount of time series data can be eventually processed with a decent speed and accuracy. We hope this paper could serve, to some extent, as a review and guideline of the time series forecasting benchmark, inspiring further attempts and researches.