Prediction of wildlife illegal trade cases based on additive factor ARIMA algorithm

Illegal wildlife trade is the fourth largest illegal trade in the world, with an annual scale of 26.5 billion US dollars, not only exacerbates biodiversity loss, but also poses a serious threat to ecosystem stability. This paper takes time series prediction as the core, and introduces additive factor ARIMA algorithm to construct dynamic prediction model of illegal wildlife trade cases. By integrating the time series characteristics of the number of illegal wildlife trade cases from 1999 to 2022. After the stationarity of the sequence was determined by ADF test, the ARIMA(p,d,q) parameter combination was optimized by grid search method, and the optimal algorithm ARIMA(2,1,2) was screened by AIC. The results of the forecast show that the wildlife trade cases will continue to increase in 5 years without considering the influence factors such as the reduction of illegal wildlife trade, and will continue to decrease in 5 years after adding many factors such as the reduction of illegal trade.