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一种轻量级的神经网络方法用于预测国家国内生产总值 (LightNet-GDP) 用回归基准
Ch V Raghavendran1, K Chandra Mouli2, S Bhargavi Latha3
1Department of IT, Aditya University, Surampalem, Andhra Pradesh, India.
Scientific reports
|January 29, 2026
概括
与传统的回归模型相比,一种新的轻量级神经网络显著提高了国内生产总值 (GDP) 预测准确度. 这种先进的模型为经济预测提供了卓越的性能和弹性.
科学领域:
- 经济学 经济学 经济学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 国内生产总值 (GDP) 是一个国家的经济健康的关键指标.
- 准确的GDP预测对于财政政策,投资和社会经济规划至关重要.
研究的目的:
- 为GDP预测引入和评估一种独特的轻量级神经网络架构.
- 将其性能与已建立的回归模型进行比较.
主要方法:
- 广泛的数据预处理,包括降噪,归算和规范化.
- 探索性数据分析 (EDA) 用于模式识别.
- 基准模型的开发和优化 (线性回归,,拉索,决策树,XGBoost).
- 实现一种新型神经网络,结合剩余学习,批量规范化,中断和内核约束.
- 使用MAE,RMSE,R2分数,MAPE和RMSLE等指标进行严格的评估.
- 五倍交叉验证和对结构噪声干扰的稳定性测试.
主要成果:
- 拟议的轻量级神经网络在所有评估指标上都表现出色.
- 与基准模型相比,它实现了更高的培训准确性和更高的概括性.
- 该模型表现出高弹性,稳定性比为1.02.
- 它在预测准确性和计算成本方面明显优于传统方法.
结论:
- 新型轻量级神经网络为GDP预测提供了高效和弹性解决方案.
- 它为增强经济预测和规划提供了有价值的工具.
- 该模型的架构平衡了性能与实用应用的简单性.
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