Forecasting Zhejiang Province's GDP Using a CNN-LSTM Model
DOI:
https://doi.org/10.54097/bmq2dy63Keywords:
CNN; LSTM; CNN-LSTM model; Zhejiang province; GDP.Abstract
Zhejiang province has experienced notable economic growth in recent years. Despite this, achieving sustainable high-quality economic development presents complex challenges and uncertainties. This study employs advanced neural network methodologies, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and an integrated CNN-LSTM model, to predict Zhejiang's economic trajectory. Our empirical analysis demonstrates the proficiency of neural networks in delivering reasonably precise economic forecasts, despite inherent prediction residuals. A comparative assessment indicates that the composite CNN-LSTM model surpasses the individual CNN and LSTM models in accuracy, providing a more reliable forecasting instrument for Zhejiang's high-quality economic progression.
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