Sales forecasting using WaveNet within the framework of the Kaggle competition
This work provides an incremental solution for improving sales forecasting accuracy in retail, specifically for grocery stores.
The authors tackled the problem of grocery sales forecasting using a dilated convolutional neural network (WaveNet) on time series data, achieving 2nd place in a Kaggle competition.
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.