Optimizing Sales Forecasting in e-Commerce with ARIMA and LSTM Models

Konstantinos Vavliakis, Konstantinos Vavliakis, Andreas Siailis, Andreas Symeonidis

2021

Abstract

Sales forecasting is the process of estimating future revenue by predicting the amount of product or services a sales unit will sell in the near future. Although significant advances have been made in developing sales forecasting techniques over the past decades, the problem is so diverse and multi-dimensional that only in a few cases high accuracy predictions can be achieved. In this work, we propose a new hybrid model that is suitable for modeling linear and non-linear sales trends by combining an ARIMA (autoregressive integrated moving average) model with an LSTM (Long short-term memory) neural network. The primary focus of our work is predicting e-commerce sales, so we incorporated in our solution the value of the final sale, as it greatly affects sales in highly competitive and price-sensitive environments like e-commerce. We compare the proposed solution against three competitive solutions using a dataset coming from a real-life e-commerce store, and we show that our solution outperforms all three competing models.

Paper Citation

in Harvard Style

Vavliakis K., Siailis A. and Symeonidis A. (2021). Optimizing Sales Forecasting in e-Commerce with ARIMA and LSTM Models. In Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-536-4, pages 299-306. DOI: 10.5220/0010659500003058

in Bibtex Style

@conference{webist21,
author={Konstantinos Vavliakis and Andreas Siailis and Andreas Symeonidis},
title={Optimizing Sales Forecasting in e-Commerce with ARIMA and LSTM Models},
booktitle={Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2021},
pages={299-306},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010659500003058},
isbn={978-989-758-536-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Optimizing Sales Forecasting in e-Commerce with ARIMA and LSTM Models
SN - 978-989-758-536-4
AU - Vavliakis K.
AU - Siailis A.
AU - Symeonidis A.
PY - 2021
SP - 299
EP - 306
DO - 10.5220/0010659500003058