Fundamental concepts and issues in multivariate time series analysis
William W. S. Wei · Wiley series in probability and statistics · 2019
Multivariate time series analysis methods are needed to properly analyze these data in a study, and these are different from standard statistical theory and methods based on random samples that assume independence. Dependence is the fundamental nature of the time series. The use of highly correlated high-dimensional time series data introduces many complications and challenges. The fundamental characteristic of a multivariate time series is that its observations depend not only on component but also time. A multiple regression is known to be a useful statistical model that describes a relationship between a response variable and several predictor variables. The error term in the model is normally assumed to be uncorrelated noise with zero mean and constant variance. Because of high-speed internet and the power and speed of the new generation of computers, a researcher now faces some very challenging phenomena.