# A network-based transfer learning approach to improve sales forecasting of new products

arXiv: https://arxiv.org/abs/2005.06978 · HTML: https://mlsanity.com/paper/2005.06978/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2020.

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

- [Multi-Variate Time Series Forecasting on Variable Subsets](https://mlsanity.com/paper/2206.12626/) (2022)
- [The impact of data set similarity and diversity on transfer learning success in time series forecasting](https://mlsanity.com/paper/2404.06198/) (2024)
- [Performance of Deep Learning models with transfer learning for multiple-step-ahead forecasts in monthly time series](https://mlsanity.com/paper/2203.11196/) (2022)

## Abstract (arXiv)

Data-driven methods -- such as machine learning and time series forecasting -- are widely used for sales forecasting in the food retail domain. However, for newly introduced products insufficient training data is available to train accurate models. In this case, human expert systems are implemented to improve prediction performance. Human experts rely on their implicit and explicit domain knowledge and transfer knowledge about historical sales of similar products to forecast new product sales. By applying the concept of Transfer Learning, we propose an analytical approach to transfer knowledge between listed stock products and new products. A network-based Transfer Learning approach for deep neural networks is designed to investigate the efficiency of Transfer Learning in the domain of food sales forecasting. Furthermore, we examine how knowledge can be shared across different products and how to identify the products most suitable for transfer. To test the proposed approach, we conduct a comprehensive case study for a newly introduced product, based on data of an Austrian food retailing company. The experimental results show, that the prediction accuracy of deep neural networks for food sales forecasting can be effectively increased using the proposed approach.