Author(s)

Yutong Li ¹

Affiliation(s)

¹ College of Rail Transportation, Suzhou University, Suzhou, China

Corresponding Author

Yutong Li

ABSTRACT

The illegal trade in wildlife is estimated to be worth $26.5 billion annually, making it the fourth largest illegal trade in the world. The main content of this paper is to build a data-driven model to reduce illegal wildlife trade. Searching the governments of all the countries that decided to collect the subject for the five-year project, the search for the United States was particularly prominent, choosing the United States as the main place where the illicit trade took place. Further searches were conducted to collect relevant indicators, classify them, and categorize the main indicators of power, resources, and benefits into categories. Then, three main indicators and four dependent variables were selected to construct partial least squares regression analysis, and the relationship between each dependent variable and the three indicators was analyzed. To describe the difference before and after the 5-year intervention, a linear regression model was used directly. Therefore, other indicators not previously selected were analyzed, and new indicators were further developed based on the description of the three main indicators in the Statistical Yearbook of the United States, including the debt of other sectors to the domestic economy as a new additional driver and energy use as a resource. Start with a direct forecast using regular data to get a look at the next 10 years with and without real-time projects. Using the rules of the five-year plan again, the annual data for the next five years is interpolated to get a data set that can be used to predict the next ten years. The effects of different prediction models were weighted. To sum up, this paper builds a prediction model of illegal wildlife trade based on data collected from the Internet and public databases.

KEYWORDS

Illegal wildlife trade, linear regression, ARIMA, predicting models

CITE THIS PAPER

Yutong Li, Research on wild illegal trade based on regression analysis and ARIMA prediction model. Information Systems and Economics (2024) Vol. 5: 192-204. DOI: http://dx.doi.org/10.23977/infse.2024.050225.

REFERENCES

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[2] Krishnamoorthy U, Karthika V, Mathumitha M K, et al. Learned prediction of cholesterol and glucose using ARIMA and LSTM models–A comparison[J]. Results in Control and Optimization, 2024, 14: 100362.
[3] Zhang J, Liu H, Bai W, et al. A hybrid approach of wavelet transform, ARIMA and LSTM model for the share price index futures forecasting[J]. The North American Journal of Economics and Finance, 2024, 69: 102022.
[4] Meeks D, Morton O, Edwards D P. Wildlife farming: Balancing economic and conservation interests in the face of illegal wildlife trade[J]. People and Nature, 2024.

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