Multivariate time series analysisForecasting in geophysical time series is a challenging problem with numerous applications. The presence of correlation (i.e.聽spatial correlation across several sites and time correlation within each site) poses difficulties with respect to traditional modeling, computation ...
Based on the successful Introduction to Multiple Time Series Analysis by Helmut Lütkepohl, published in 1991/1993 Totally revised and with new chapters on cointegration analysis, structural vector autoregressions, cointegrated VARMA processes and multivariate ARCH models Includes supplementary material: sn....
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...
This reference work and graduate level textbook considers a wide range of models and methods for analyzing and forecasting multiple time series. The models covered include vector autoregressive, cointegrated, vector autoregressive moving average, multivariate ARCH and periodic processes as well as dynamic ...
We consider the multiple change-point problem for multivariate time series, including strongly dependent processes, with an unknown number of change-points. We assume that the covariance structure of the series changes abruptly at some unknown common change-point times. The proposed adaptive method is...
Examples of multivariate modeling in retrospective analysis are found inPaul et al. (2008). However, there are few examples of prospective analysis in this area (Paul and Held, 2011). View chapterExplore book A review on time series data mining ...
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...
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 ...
Principal component analysis of multidimensional psychiatric states Multidimensional psychiatric time-series data were decomposed into orthogonal principal components. The top four components explained ~60% of the variance (PC1: 24.1%, PC2: 14.3%, PC3: 11.0%, PC4: 10.6%, in total 59.9%), so we ...
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 ...