关于在经济数据预测中具有强大的基于预测的损失函数的循环神经网络模型
Wisnowan Hendy Saputra1, Rinda Nariswari2, Matthew Owen2
1Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, 11530, Indonesia.
MethodsX
|December 1, 2025
概括
本研究介绍了基于预测的循环神经网络 (E-RNN),以改善非静止数据的时间序列预测. 与标准的循环神经网络 (RNN) 相比,E-RNN提供了更强大的基于场景的预测.
科学领域:
- 机器学习 机器学习
- 计量经济学 计量经济学
- 时间序列分析时间序列分析
背景情况:
- 循环神经网络 (RNN),包括LSTM和GRU,是连续数据的标准,但与非静止和异质时间序列作斗争.
- 它们的限制源于对称损失函数 (例如,MSE) 假设数据均性.
- 这阻碍了对各种数据模式和条件的准确预测.
研究的目的:
- 提出一种新的基于预测的循环神经网络 (E-RNN) 框架,将预测回归整合到RNN中.
- 开发和比较E-LSTM和E-GRU变体,以进行高级时间序列预测.
- 通过调整一个不对称参数 (τ) 来实现基于场景的预测 (从悲观到乐观).
主要方法:
- 开发出基于的循环神经网络 (E-RNN) 变体:E-LSTM和E-GRU.
- 利用一个不对称的最小平方损失函数来建模超出中心趋势的条件数据分布.
- 实施基于预测的通用近似交叉验证 (EGACV) 进行稳健的模型选择.
主要成果:
- 在预测印尼季度经济增长方面,E-RNN模型表现出卓越的表现.
- 与标准RNN相比,获得了较低的EGACV分数和更高的预测准确性.
- 在波动性季度到季度 (qtq) 数据上显示出显著的改进,提高了预测可靠性.
结论:
- 电子RNN提供适应性预测模型,适应数据分布的变化,克服同质性假设.
- EGACV标准提供了一种可靠的方法来平衡模型适合性和复杂性.
- 该框架允许通过调整不对称参数 (τ) 来生成各种预测场景.
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