基于非静止时间序列的双分解和模糊认知图的预测
Junfeng Chen1,2, Azhu Guan3, Shi Cheng4
1College of Artificial Intelligence and Automation, Hohai University, Changzhou 213200, China.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
本研究介绍了WE-HFCM模型用于可解释的时间序列预测. 与传统方法相比,这种新的方法显著提高了静态和非静态数据的预测准确性.
科学领域:
- 时间序列分析时间序列分析
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 像重复神经网络 (RNN) 这样的深度学习模型在时间序列预测方面表现出色,但缺乏解释性,阻碍了决策者信任.
- 可解释和准确的预测模型对于复杂系统中可靠的决策至关重要.
研究的目的:
- 开发一种可解释且准确的时间序列预测模型.
- 通过可解释性,增强决策者对预测模型的信任.
主要方法:
- 提出了一个新的WE-HFCM模型,将波形分解 (WD) 和经验模式分解 (EMD) 集成为信号分解.
- 高级模糊认知地图 (HFCM) 是为了解释性和推理而构建的.
- 斜坡回归被用来学习HFCM重量向量用于预测建模.
主要成果:
- WE-HFCM模型在静态和非静态数据集上都表现出卓越的预测准确性.
- 对于静态系列,WE-HFCM的精度比ARIMA高45%,比SARIMA高35%,比LSTM高16%.
- 对于非静止系列,WE-HFCM的准确度超过了ARIMA和SARIMA的69%.
结论:
- 拟议的WE-HFCM模型有效地将分解技术与可解释的模糊认知图相结合,用于准确的时间序列预测.
- 该模型在预测准确性和可解释性方面比ARIMA和LSTM等现有方法显著改进.
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