深信改善了双向LSTM用于多变量时间序列预测.
Keruo Jiang1,2, Zhen Huang1,2, Xinyan Zhou2
1State Grid Ningbo Electric Power Supply Company, Ningbo 315000, China.
Mathematical biosciences and engineering : MBE
|November 3, 2023
概括
本研究介绍了一种新的深信网络双向LSTM (DBI-BiLSTM) 模型,用于多变量时间序列预测. 在复杂的时间序列数据中,DBI-BiLSTM通过增强特征提取和空间关系学习,显著提高了预测准确性.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 多变量时间序列 (MTS) 数据很普遍,但由于复杂的时间依赖性,难以预测.
- 传统的MTS预测方法面临速度和复杂性的局限性.
- 现有的长短期记忆 (LSTM) 网络部分捕捉空间关系,浅层LSTM与高维数据作斗争.
研究的目的:
- 开发一个改进的双向LSTM (BiLSTM) 网络,称为DBI-BiLSTM,用于增强MTS预测.
- 利用深度信念网络 (DBN) 和链式结构来进行更深层次的特征提取和双向学习.
- 与现有方法相比,提高MTS预测的准确性和效率.
主要方法:
- 一个新的DBI-BiLSTM架构,将DBN层与堆叠的BiLSTM层集成在一起.
- 输入数据由DBN处理,用于全面的特征提取.
- 通过全球灵敏度分析识别的聚类特征被输入到BiLSTM层中.
- 来自浅层的输出与集群特征相结合,用于更深层的输入.
主要成果:
- DBI-BiLSTM模型在四个现实世界MTS数据集上展示了卓越的前一步预测性能.
- 与传统的人工神经网络 (ANN),深度LSTM和其他先进的LSTM变体相比,观察到显著的改善.
- 与传统的LSTM相比,数据集的百分比改善率在30.72%至85.41%之间.
结论:
- 拟议的DBI-BiLSTM有效地捕捉了复杂的空间关系,并增强了MTS数据中的特征表示.
- DBI-BiLSTM为多变量时间序列预测挑战提供了强大而准确的解决方案.
- 该模型的深度结构和双向学习能力有助于其先进的性能.
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