Research on Wildlife Trade Based on SARIMA and Multiple Regression

Abstract

At the start, this paper firstly converges the data set given in the topic to count out the data from 1990-2021. After that, uses the count function of MATLAB on the pd library to statistically analyze the family data, so as to get the greatest number of wild animal groups and species transactions, and concludes that: among the species varieties, the category of Cercopithecidae has the most number, followed by Felidae, Cebidae. among the specific genera of animals, Macaca had the highest number, followed by Papio and Potos. Next, this paper analyzes the statistical options so as to obtain the most important trade purposes, and concludes that: the analysis of the global wildlife trade records from 2003 to 2021 shows that the purposes of wildlife trade are the most used for wildlife trade, zoo use, and circus performances. After that, this paper uses the number of trade imports and exports over the years as the basic data to measure the state of the trade market, and later analyzes it by constructing the growth rate of trade transactions as an indicator to assess the state of the wildlife trade market. The concludes that: with the change of time, the economy was relatively stable in the pre-wild trade market, and there was a market turmoil in 2012, and then it stabilized. Market turbulence was observed in both 2018-2022, with a depressed status quo in the wild trade market. Then, this paper will use multiple regression analysis to analyze the number of import and export transactions of wildlife trade each year and the degree of epidemic, so that the correlation between the two can be judged by the regression coefficient, and the conclusion is: the severity of the epidemic will indeed have a certain impact on the wild trade market, and this relationship shows a typical negative correlation, that is, the higher the severity of the epidemic, the lower the import and export The lower the severity of the epidemic, the higher the import and export trade in the wild trade market. Last, the annual wildlife trade import and export quantities are used as the basic data, and the total global GDP is collected as an indicator of the global economy, so that a multiple regression model is constructed to analyze the relationship between the global wildlife trade import and export quantities and the total global GDP, and concludes that: based on this analysis, this paper concludes that the global wildlife trade ban needs to be adhered to at least for a long period of time, and that the wildlife trade does affect the global economic situation.