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

Zihao Guo; Yuhan Yang; Xinxiong Wu; Zhiyong Zheng; Weiqi Liu

doi:10.54097/kb95a961

Authors

Zihao Guo
Yuhan Yang
Xinxiong Wu
Zhiyong Zheng
Weiqi Liu

DOI:

https://doi.org/10.54097/kb95a961

Keywords:

Big Data, ARIMA, LSTM.

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.

Downloads

Download data is not yet available.

References

Karim F, Majumdar S, Darabi H. Insights into LSTM fully convolutional networks for time series classification[J]. IEEE Access, 2019, 7: 67718-67725.

Yu Y, Si X, Hu C, et al. A review of recurrent neural networks: LSTM cells and network architectures[J]. Neural computation, 2019, 31(7): 1235-1270.

Wei W, Wu H, Ma H. An autoencoder and LSTM-based traffic flow prediction method[J]. Sensors, 2019, 19(13): 2946.

Li T, Hua M, Wu X U. A hybrid CNN-LSTM model for forecasting particulate matter (PM2. 5)[J]. Ieee Access, 2020, 8: 26933-26940.

Park K, Choi Y, Choi W J, et al. LSTM-based battery remaining useful life prediction with multi-channel charging profiles[J]. Ieee Access, 2020, 8: 20786-20798.

Cui Z, Ke R, Pu Z, et al. Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values[J]. Transportation Research Part C: Emerging Technologies, 2020, 118: 102674.

Shahid F, Zameer A, Muneeb M. Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM[J]. Chaos, Solitons & Fractals, 2020, 140: 110212.

Fan D, Sun H, Yao J, et al. Well production forecasting based on ARIMA-LSTM model considering manual operations[J]. Energy, 2021, 220: 119708.

Vincent Nĳman, Thais Morcatty, Jaima H. Smith, Sadek Atoussi, Chris R. Shepherd, Penthai Siriwat, K. Anne-Isola Nekaris, and Daniel Bergin. Illegal wildlife trade - surveying open animal markets and online platforms to understand the poaching of wild cats. Biodiversity, 20(1):58–61, 2019.

ZHANG Huifeng CAO Xiaoyu, XU Bo and WEI Xinjiang. Research on price level prediction of antitumor drugs in shandong province based on lstm and arima models. China Hospital Statistics, 29(419-423), 2022.