E-commerce Retail Sales Trend Analysis and Prediction Based on the ARIMA Model

DOI

10.2991/978-94-6463-748-9_87How to use a DOI?

Keywords

E-commerce; ARIMA model; Sales forecasting; Market Shift; COVID-19

Abstract

E-commerce retail sales have grown significantly recently due to technological advancements and the COVID-19 pandemic. The importance of accurate sales forecasting in this domain is boosted drastically. The study addresses a gap in the field regarding the robustness of forecasting models in the face of abrupt market shifts. This paper delves into the prediction of e-commerce retail sales trends using the Autoregressive Integrated Moving Average (ARIMA) model and analyzes the COVID-19 pandemic’s unprecedented impact on consumer behavior and the digital economy. The research aims to evaluate the ARIMA model’s efficacy in forecasting e-commerce sales in the current market. This research employs two data-splitting methods to assess the impact of the pandemic on prediction accuracy. The study reveals that while the ARIMA model performs well in stable periods, it struggles with the volatility and unpredictability introduced by the pandemic. The model’s predictions for the post-pandemic period show significant deviations from actual values. The research concludes that despite the ARIMA model’s utility in short-term forecasting, it requires enhancement during crises like the COVID-19 pandemic.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jierui Fang
PY  - 2025
DA  - 2025/07/03
TI  - E-commerce Retail Sales Trend Analysis and Prediction Based on the ARIMA Model
BT  - Proceedings of the 2025 International Conference on Financial Risk and Investment Management (ICFRIM 2025)
PB  - Atlantis Press
SP  - 789
EP  - 797
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-748-9_87
DO  - 10.2991/978-94-6463-748-9_87
ID  - Fang2025
ER  -