The empirical analysis yielded interesting findings regarding affect regulation and coregulation within couples, demonstrating promising uses of MDFMs in analyzing multiple multivariate time series. In the end, we discussed a number of methodological issues in the applications of MDFMs and pointed out ...
Incorporating more than six chapters of new material, New Introduction to Multiple Time Series Analysis also provides extensive coverage of the vector error-correction model (VECM) for cointegrated processes, structural VARs, structural VECMs, cointegrated VARMA processes, and multivariate models for ...
Multiscale analysisMultivariate time seriesThis paper introduces a new wavelet methodology to handle dynamic co-movements of multivariate time series via extending multiple and quadruple wavelet coherence methodologies. The primary motivation of our works is to measure wavelet coherence analytically for the ...
(1957). The analysis of multiple time series. Griffin, London. Tiao G.C., Tsay R.S. (1989). Model specification in multivariate time series (with discussion). J. Royal Statist. Soc. B 51, 157–213. Wahlberg B., Hannan, E.J. (1993). Parametric signal modelling using laguerre ...
Multivariate time series anomaly detection (MTSAD) plays a crucial role in the Internet of Things (IoT), identifying device malfunction or system attacks. Graph neural networks (GNNs) are widely applied in MTSAD to capture the spatial features among sensors. However, GNN requires an explicit grap...
analysis, structural vector autoregressions, cointegrated VARMA processes and multivariate ARCH models. The book bridges the gap to the difficult technical literature on the topic. It is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields...
In calculating the weights, a, b1,…,bn, regression analysis ensures maximal prediction of the dependent variable from the set of independent variables. This is usually done by least squares estimation. This approach can be applied to analyze multivariate time series data when one of the ...
Examples of multivariate modeling in retrospective analysis are found in Paul et al. (2008). However, there are few examples of prospective analysis in this area (Paul and Held, 2011). View chapter Mini review A review on time series data mining Engineering Applications of Artificial Intelligence...
Multivariate chaotic time series is widely present in nature, such as in economy, society, industry and other fields.Modeling and predicting multivariate time series will help human to better manage, control, and make decision. A prediction method based on multiple kernel extreme learning machine is...
estimatoroperating on time series is included. 处理传感器阵列上接收到的信号来确定发射器的位置非常有趣,已经应用于很多特殊情况假设下。 一般问题考虑在任意协方差矩阵的噪声/干扰环境中具有任意位置和任意方向特性(增益/相位/极化)的传感器。 本报告首先关注该问题的多发射器方面,其次关注解决方案的通用性。 给出...