ENTERPRISE AI ANALYSIS
Quarterly GDP Forecasting with a Decomposition-Based Approach: Evidence from Guangdong Province
Accurate forecasting of regional gross domestic product (GDP) is essential for evaluating economic performance and supporting macroeconomic policy formulation. This study proposes a decomposition-based forecasting framework that integrates Seasonal-Trend decomposition using Loess (STL) with linear regression (LR) for quarterly GDP forecasting. The empirical results indicate that the proposed STL-LR framework achieves superior forecasting accuracy and robustness relative to benchmark models. These findings demonstrate that combining seasonal-trend decomposition with regression-based modeling provides an effective and interpretable approach for regional GDP forecasting and economic analysis.
Executive Impact & Strategic Value
The proposed STL-LR framework offers a robust and interpretable solution for forecasting regional GDP, addressing the challenges of heterogeneous dynamics in economic time series. By explicitly decomposing GDP into trend, seasonal, and residual components and modeling their effects with linear regression, the framework provides superior accuracy and stability. This translates to more reliable economic performance evaluation, informed macroeconomic policy formulation, and enhanced regional development planning for areas like Guangdong Province.
881.75
Lowest RMSE (STL-LR)
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Deep Analysis & Enterprise Applications
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881.75
Lowest RMSE (STL-LR)
| Model | RMSE | MAE |
|---|---|---|
| STL-LR | 881.75 | 668.29 |
| STL-XGBoost | 1708.19 | 1492.89 |
| LR | 1202.64 | 1138.75 |
| XGBoost | 1324.42 | 1133.96 |
| SNaive | 1160.06 | 1097.91 |
| MA | 2094.81 | 1552.02 |
Key Finding: Decomposition-Based Approach Superiority
Superior Accuracy & Robustness: The proposed STL-LR framework significantly outperforms conventional time-series and nonlinear models in forecasting accuracy and robustness, demonstrating the effectiveness of explicitly addressing heterogeneous dynamics in regional GDP.
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Enterprise Process Flow
Raw Quarterly GDP Series
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STL Decomposition (Trend, Seasonal, Remainder)
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Lagged Components as Explanatory Variables
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Linear Regression Model Training
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One-Step-Ahead Prediction
Guangdong Province GDP Dynamics
The analysis of Guangdong Province's quarterly GDP data (2005Q1-2025Q3) reveals a persistent long-term growth trend and stable seasonal regularities. First quarters consistently show lower GDP, while fourth quarters show the highest. STL decomposition effectively isolates these components, providing a structured representation crucial for accurate forecasting.
- Strong export orientation and diversified industrial structure.
- Pronounced seasonal production and consumption cycles.
- STL decomposition confirms suitability for capturing these heterogeneous dynamics.
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