Establishment of Economic Analysis Model Based on Artificial Intelligence Technology

With the continuous evolution of artificial intelligence technology, its integration into economic analysis models is becoming increasingly prevalent. This paper employs the Lasso Back Propagation neural network method to conduct financial analysis and prediction for major global economies, focusing on total Gross Domestic Product, combined Gross Domestic Product growth rate, and Consumer Price Index. The real Gross Domestic Product of the top 30 countries in the global ranking is meticulously analyzed and categorized into various economic types. This categorization, coupled with the utilization of neural network multi-hidden layer variable analysis, facilitates the analysis and prediction of national economic trends. The findings reveal that overall economic growth among the top 30 countries is sluggish, albeit showing a growth trajectory. However, the driving force for economic growth remains notably inadequate. Moreover, employing a single time series model effectively predicts Gross Domestic Product and Consumer Price Index growth rates, alongside other macroeconomic indicators. Notably, the absence of autocorrelation in the fitting residual series underscores the applicability of the time series method for combined forecasting, affirming the robustness of the predictive framework.

@article{Shi2024,
  title     = {Establishment of Economic Analysis Model Based on Artificial Intelligence Technology},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {5},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Jiqing Shi},
  doi       = {10.14569/IJACSA.2024.0150586},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150586}
}