Research on Sales Forecast of Fresh Food Industry Based on ARIMA-Transformer Model

Xiaoli Zhang, Huailiang Zhang, Yanyu Gong, Xue Zhang, Haifeng Wang

2023

Abstract

In addition to its high perishability, fresh food also has a strong timeliness. In order to reduce costs and improve efficiency, it is necessary for enterprises to accurately predict the sales volume of fresh food. This paper examines how order planning and production output are out of balance in the sales process of fresh food industries, and presents a time series high-frequency trading big data forecasting model based on the ARIMA-Transformer combined forecasting model, along with a quantitative analysis of the MAPE and RMSPE evaluation indexes. Based on the experimental results, the MAPE of the ARIMA-Transformer forecasting model is 0.171 percent lower than the MAPE of the LSTM, ARIMA, and Transformer models, and the RMSPE is 0.306 percent lower than that of the LSTM model, proving its rationality and superiority in predicting fresh food sales volumes.

Paper Citation

in Harvard Style

Zhang X., Zhang H., Gong Y., Zhang X. and Wang H. (2023). Research on Sales Forecast of Fresh Food Industry Based on ARIMA-Transformer Model. In Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT; ISBN 978-989-758-677-4, SciTePress, pages 397-400. DOI: 10.5220/0012284800003807

in Bibtex Style

@conference{anit23,
author={Xiaoli Zhang and Huailiang Zhang and Yanyu Gong and Xue Zhang and Haifeng Wang},
title={Research on Sales Forecast of Fresh Food Industry Based on ARIMA-Transformer Model},
booktitle={Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT},
year={2023},
pages={397-400},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012284800003807},
isbn={978-989-758-677-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT
TI - Research on Sales Forecast of Fresh Food Industry Based on ARIMA-Transformer Model
SN - 978-989-758-677-4
AU - Zhang X.
AU - Zhang H.
AU - Gong Y.
AU - Zhang X.
AU - Wang H.
PY - 2023
SP - 397
EP - 400
DO - 10.5220/0012284800003807
PB - SciTePress