Inflation Prediction Method Based on Deep Learning.

Area of Science:

• Economics
• Data Science
• Computational Finance

Background:

• Accurate inflation rate forecasting is crucial for monetary policy, economic stability, and investment decisions.
• Existing linear models (AR, VAR) struggle to capture complex nonlinear relationships and historical data nuances.
• Limitations in current prediction accuracy necessitate advanced modeling techniques.

Purpose of the Study:

• To address the limitations of traditional linear models in inflation forecasting.
• To leverage deep learning's ability to mine nonlinear relationships and long-term time series dynamics.
• To evaluate the effectiveness of a Gated Recurrent Unit (GRU-RNN) model for predicting China's inflation rate.

Main Methods:

• Utilized a deep learning approach, specifically the Gated Recurrent Unit (GRU-RNN) model.
• Trained and analyzed the Consumer Price Index (CPI) indicators using historical data.
• Compared the GRU-RNN model's performance against traditional forecasting models.

Main Results:

• The GRU-RNN model demonstrated strong performance in predicting China's inflation rate.
• Experimental results confirmed the model's effectiveness in capturing complex inflation dynamics.
• The proposed GRU-RNN method significantly outperformed traditional models in predictive accuracy.

Conclusions:

• Deep learning models, like GRU-RNN, offer superior capabilities for inflation forecasting compared to linear methods.
• The GRU-RNN model provides a more effective strategy for predicting inflation rates, enhancing economic analysis.
• Accurate inflation prediction using advanced models supports better monetary policy and financial decision-making.