Pork Price Prediction Using LSTM Model Based on a New Dataset

Ziya Yang, Ruijie Zhang, Zheng Yin, Yucheng Zhu

2022

Abstract

During the epidemic period, some of prevention and control measures have exacerbated the contradiction be-tween supply and demand, which seriously affected the national economy and people's livelihood. In recent years, with the development of computer science and the spread of the digital economy, many studies have turned their attention to agricultural product price forecasting. Nevertheless, the application of intelligent meth-od in this area is still lacking. Thus, this paper uses ARIMA model and LSTM model for pork price prediction based on a new dataset, trying to figure out a better model and provide guidance for national price manage-ment. According to the analysis, LSTM model outperforms ARIMA model in both short-term and long-term prediction, which overcomes the problem of long-term dependency. However, the data under the epidemic is not sufficient, which limits the extraction of effective information, affecting the accuracy of model predictions to some extent. It is suggested that following research should collect more data in the context of covid-19 and adopt better dimensionality reduction method to achieve better results.

Paper Citation

in Harvard Style

Yang Z., Zhang R., Yin Z. and Zhu Y. (2022). Pork Price Prediction Using LSTM Model Based on a New Dataset. In Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI; ISBN 978-989-758-620-0, SciTePress, pages 423-429. DOI: 10.5220/0011738900003607

in Bibtex Style

@conference{icpdi22,
author={Ziya Yang and Ruijie Zhang and Zheng Yin and Yucheng Zhu},
title={Pork Price Prediction Using LSTM Model Based on a New Dataset},
booktitle={Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI},
year={2022},
pages={423-429},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011738900003607},
isbn={978-989-758-620-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology - Volume 1: ICPDI
TI - Pork Price Prediction Using LSTM Model Based on a New Dataset
SN - 978-989-758-620-0
AU - Yang Z.
AU - Zhang R.
AU - Yin Z.
AU - Zhu Y.
PY - 2022
SP - 423
EP - 429
DO - 10.5220/0011738900003607
PB - SciTePress