Prediction of Liaoning province steel import and export trade based on deep learning models

Abstract

In the field of deep learning, time series forecasting, particularly for economic and trade data, is a critical area of research. This study introduces a hybrid of auto regressive integrated moving average and gated recurrent unit (ARIMA‐GRU) to enhance the prediction of steel import and export trade in Liaoning Province, addressing the limitations of traditional time series methods. Traditional models like ARIMA excel with linear data but often struggle with non‐linear patterns and long‐term dependencies. The ARIMA‐GRU model combines ARIMA's linear data analysis with GRU's proficiency in non‐linear pattern recognition, effectively capturing complex dynamics in economic datasets. Our experiments show that this hybrid approach surpasses traditional models in accuracy and reliability for forecasting steel trade, providing valuable insights for economic planning and strategic decision‐making. This innovative approach not only advances the field of economic forecasting but also demonstrates the potential of integrating deep learning techniques in complex data analysis.