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Topics in Multivariate Time Series Analysis: Statistical Control, Dimension Reduction Visualization and Thir Business Applications
Topics in Multivariate Time Series Analysis Statistical Control Dimension Reduction Visualization Their Business Applications
2014/10/28
Most business processes are, by nature, multivariate and autocorrelated. Highdimensionality is rooted in processes where more than one variable is considered simultaneously to provide a more comprehen...
Coherence and phase synchronization：generalization to pairs of multivariate time series，and removal of zero-lag contributions
Coherence and phase synchronization generalization multivariate time series zero-lag contributions
2010/4/29
Coherence and phase synchronization between time series corresponding to
different spatial locations are usually interpreted as indicators of the “connectivity”
between locations. In neurophysiology...
Structural shrinkage of nonparametric spectral estimators for multivariate time series
structural shrinkage nonparametric spectral estimators multivariate time series
2009/9/16
In this paper we investigate the performance of periodogram based estimators of the spectral density matrix of possibly high-dimensional time series. We suggest and study shrinkage as a remedy against...
Ultrametric Wavelet Regression of Multivariate Time Series：Application to Colombian Conflict Analysis
Ultrametric Wavelet Regression Multivariate Time Series Colombian Conflict Analysis
2010/3/18
We first pursue the study of how hierarchy provides a well-adapted tool
for the analysis of change. Then, using a time sequence-constrained hi-
erarchical clustering, we develop the practical aspect...
Sparse Causal Discovery in Multivariate Time Series
Vector Autoregressive Model Granger Causality Group Lasso Multiple Testing
2010/3/17
Our goal is to estimate causal interactions in multivariate time series.
Using vector autoregressive (VAR) models, these can be defined based on
non-vanishing coecients belonging to respective time...
Regularly varying multivariate time series
clusters of extremes extremal index heavy tails mixing moving average multivariate regular variation point processes
2010/4/30
A multivariate, stationary time series is said to be jointly regularly
varying if all its finite-dimensional distributions are multivariate regularly
varying. This property is shown to be equivalent...