文献类型：期刊文献

英文题名：Online estimation and dynamic forecasting of multivariate time series streams

作者：Quan, Mingxue[1];Long, Yonghong[1];Tian, Miao[2]

机构：[1]Renmin Univ China, Dept Math, Beijing 100872, Peoples R China;[2]East China Univ Sci & Technol, Dept Math, Shanghai 200237, Peoples R China

年份：2026

卷号：224

外文期刊名：COMPUTATIONAL STATISTICS & DATA ANALYSIS

收录：;EI(收录号：20262620998442);WOS:【SCI-EXPANDED(收录号:WOS:001809003100001)】；

基金：Quan's research was supported by National Natural Science Foundation of China (No.12401396) , China Postdoctoral Science Foundation (No.2024M753594, No.GZC20241957) , and the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (No.24XNKJ19) .

语种：英文

外文关键词：Streaming data; Vector autoregressive model; Real-time forecasting; Time efficiency.

摘要：For multivariate time series streams, each sequence grows indefinitely over time and exhibits complex interdependencies with others. Such data have become increasingly prevalent across various fields. These streams are processed sequentially in the order of generation, with each data point discarded immediately after processing. The one-pass processing constraint, combined with the lack of historical data, poses significant challenges that traditional methods struggle to overcome effectively. To address these issues, a comprehensive framework for online estimation and dynamic forecasting based on the vector autoregressive (VAR) model is proposed. Beyond online updating, the framework also dynamically estimates the lag order, accounting for the resulting changes in the dimensionality of the summary statistics and controls error accumulation in the covariance matrix estimation. Theoretical results show that the proposed online estimators are asymptotically equivalent to their oracle counterparts, while the resulting dynamic forecasts exhibit stable predictive performance throughout the online updating process. Finally, extensive numerical experiments and real-world data analyses demonstrate the practical effectiveness and robustness of the proposed approach, highlighting its suitability for real-time applications under memory and computational constraints.