Research on trade quantity prediction on ARIMA and Long Short-Term Memory Networks

Abstract

In the face of the enormous threat that illegal wildlife trade poses to global ecology and biodiversity. In this paper, two prediction models, ARIMA and LSTM, are used for comparative experiments. By analyzing the data of global illegal wildlife trade from 1995 to 2021, two prediction models, ARIMA and LSTM, are established to obtain the prediction results of global illegal wildlife trade from 2022 to 2029 respectively. The experimental results show that both the ARIMA prediction model and the LSTM prediction model show an upward trend in the prediction of the amount of illegal wildlife trade. Especially noteworthy is that the prediction results of the LSTM model show a more obvious upward trend and larger prediction values compared with the ARIMA model. The projections tell us the grim story of the illegal wildlife trade. If we do not take urgent and effective interventions, the scale of the illegal wildlife trade will continue to show a worrying trend and could rise further. This not only poses a serious threat to global biodiversity, but also has potential negative impacts on ecological balance, economic and social stability. These projections can effectively guide efforts to reduce the amount of illegal wildlife trade.