Open AccessApplication of time series model and deep learning method in measuring the impact of COVID-19 on agriculture in Hubei, China
Author(s) -
Yonghong Zou,
Jiaojiao Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1941/1/012073
Subject(s) - autoregressive integrated moving average , agriculture , quarter (canadian coin) , time series , gross domestic product , animal husbandry , agricultural economics , product (mathematics) , index (typography) , china , artificial neural network , geography , econometrics , computer science , statistics , economics , mathematics , artificial intelligence , economic growth , geometry , archaeology , world wide web
Taking the agricultural situation of Hubei Province as the research object, this paper uses the time series ARIMA model, ARMA model and deep learning LSTM neural network model to explore the impact of COVID-19 on the agriculture of Hubei Province. Three main indicators are screened out to measure the development of agricultural economy, that is, gross regional product, gross output value of agriculture, forestry, animal husbandry and fishery, agricultural product production price index. Based on the quarterly data of indicators from 2001 to 2019 from the National Bureau of Statistics, three indicators in Hubei Province in the first quarter and the second quarter of 2020 are predicted by using the deep learning-based time series ARIMA model, ARMA model and LSTM neural network model. By comparing the predicted data with the real data, the impact of COVID-19 is measured on the agricultural situation of Hubei Province. It was found that COVID-19 had a great impact on the agricultural situation of Hubei Province in both of the first and second quarters of 2020, with the impact in the first quarter being greater than that in the second quarter. At the same time, the prediction accuracy of the two methods is compared to find that the time series model is more effective and reliable in predicting the agricultural product price index. The LSTM neural network model with a long and short term memory has a good prediction effect on the regional gross product and the total output value of agriculture, forestry, animal husbandry and fishery.