计算机应用与软件2024，Vol.41Issue(5)：233-239,7.DOI:10.3969/j.issn.1000-386x.2024.05.036
改进DTW下界约束的Granger多元时序LSTM预测模型
GRANGER MULTIVARIATE TIME SERIES LSTM PREDICTION MODEL WITH IMPROVED DTW LOWER BOUND CONSTRAINT
摘要
Abstract
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.关键词
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Key words
Dynamic time warping/Long and short-term memory network/Granger causality/Hierarchical filter分类
信息技术与安全科学引用本文复制引用
许凤魁,孙士保,贾少勇,王静..改进DTW下界约束的Granger多元时序LSTM预测模型[J].计算机应用与软件,2024,41(5):233-239,7.基金项目
国家自然科学基金项目(51474095) （51474095）
河南省重点攻关项目(152102210277) （152102210277）
河南省高校科技创新团队支持计划项目(17IRTSTHN010) （17IRTSTHN010）
河南科技大学科技创新团队项目(2015XTD011) （2015XTD011）
河南科技大学重大产学研合作培育基金项目(2015ZDCXY03). （2015ZDCXY03）
