Rolling Iterative Prediction for Correlated Multivariate Time Series  

作　　者：Peng Liu Qiong Han Xiao Yang 

机构地区：[1]College of Computer Science and Technology,Harbin Engineering University,Harbin 150001,China

出　　处：《国际计算机前沿大会会议论文集》2023年第1期433-452,共20页International Conference of Pioneering Computer Scientists, Engineers and Educators（ICPCSEE）

基　　金：This work was supported by the National Key R&D Program of China under Grant No.2020YFB1710200.

摘　　要：Correlated multivariate time series prediction is an effective tool for discovering the chang rules of temporal data,but it is challenging tofind these rules.Recently,deep learning methods have made it possible to predict high-dimensional and complex multivariate time series data.However,these methods cannot capture or predict potential mutation signals of time series,leading to a lag in data prediction trends and large errors.Moreover,it is difficult to capture dependencies of the data,especially when the data is sparse and the time intervals are large.In this paper,we proposed a prediction approach that leverages both propagation dynamics and deep learning,called Rolling Iterative Prediction(RIP).In RIP method,the Time-Delay Moving Average(TDMA)is used to carry out maximum likelihood reduction on the raw data,and the propagation dynamics model is applied to obtain the potential propagation parameters data,and dynamic properties of the correlated multivariate time series are clearly established.Long Short-Term Memory(LSTM)is applied to capture the time dependencies of data,and the medium and long-term Rolling Iterative Prediction method is established by alternately estimating parameters and predicting time series.Experiments are performed on the data of the Corona Virus Disease 2019(COVID-19)in China,France,and South Korea.Experimental results show that the real distribution of the epidemic data is well restored,the prediction accuracy is better than baseline methods.

关 键 词：Time Series Prediction Correlated Multivariate Time Series Trend Prediction of Infectious Disease Rolling Circulation 

分 类 号：TN9[电子电信—信息与通信工程]