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Related Experiment Video

Updated: Sep 28, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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China's Economic Forecast Based on Machine Learning and Quantitative Easing.

Chang Qiu1

  • 1School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China.

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|April 5, 2022
PubMed
Summary
This summary is machine-generated.

The ARIMA model demonstrates a clear advantage in forecasting China

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Area of Science:

  • Econometrics
  • International Trade
  • Time Series Analysis

Background:

  • China's export value to the US is influenced by multiple economic factors.
  • Accurate forecasting models are crucial for trade policy and business strategy.

Purpose of the Study:

  • To model and forecast China's export value to the US.
  • To compare the efficacy of BP neural network, ARIMA, and AR-GARCH models.

Main Methods:

  • Utilized six variables: export value, real exchange rate, Chinese GDP, US Industrial Production Index (IPI), and seasonal variables.
  • Employed BP neural network, ARIMA, and AR-GARCH models for analysis.
  • Evaluated model performance using error indicators for simulation and prediction accuracy.

Main Results:

  • All three models provided satisfactory simulation and forecasting results.
  • The ARIMA model exhibited a distinct advantage in accuracy compared to BP neural network and AR-GARCH.
  • Identified specific reasons for the performance differences among the models.

Conclusions:

  • The ARIMA model is recommended for its superior performance in forecasting China's exports to the US.
  • Analysis provides insights into factors affecting Sino-US trade.
  • Offers data-driven suggestions for enhancing China's export performance.