首页|改进DTW下界约束的Granger多元时序LSTM预测模型

改进DTW下界约束的Granger多元时序LSTM预测模型

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原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
多元时序的因果预测研究是探讨复杂网络响应关系的热点问题。提出一种通过DTW的下界约束组合并改进的层级过滤器，与格兰杰因果验证法相结合验证其因果统计量，挖掘出有效信息实现有效降维，进一步输入LSTM预测模型进行因果预测。仿真实验利用开源的空气质量数据集进行定量和定性对比验证，该方法的损失函数训练曲线和测试曲线有较好的拟合度，表明该因果预测法是可行且有效的。
GRANGER MULTIVARIATE TIME SERIES LSTM PREDICTION MODEL WITH IMPROVED DTW LOWER BOUND CONSTRAINT
The research on the causal prediction of multivariate time series is a hot issue to explore the relationship between complex network-driven responses.This paper proposes a hierarchical filter method that combines and improves the lower bound constraints of dynamic time warping(DTW).This method was combined with Granger causality to verify the causal statistics,so that it dug out effective value information to achieve effective dimensionality reduction.And it was inputted into LSTM prediction model to make causal predictions.The simulation experiment used open-source air quality time series data sets for quantitative and qualitative comparison and verification.It is found that the training curve and the test curve in its loss function curve have a better fit,which shows that the causal prediction method is feasible and effective.

Dynamic time warpingLong and short-term memory networkGranger causalityHierarchical filter

许凤魁、孙士保、贾少勇、王静

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河南科技大学信息工程学院 河南洛阳 471023

动态时间弯曲 长短时记忆网络 格兰杰因果关系 层级过滤器

国家自然科学基金河南省重点攻关项目河南省高等学校科技创新团队支持计划河南科技大学科技创新团队项目河南科技大学重大产学研合作培育基金

5147409515210221027717IRTSTHN0102015XTD0112015ZDCXY03

2024

10.3969/j.issn.1000-386x.2024.05.036

计算机应用与软件
上海市计算技术研究所 上海计算机软件技术开发中心

计算机应用与软件

CSTPCD北大核心
影响因子：0.615
ISSN：1000-386X
年,卷(期)：2024.41(5)
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