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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...
Segmentation analysis on a multivariate time series of the foreign exchange rates
finite multivariate Gaussian mixture Jensen-Shannon divergence variance-covariance matrix cross-sectional analysis
2012/6/2
This study considers the multivariate segmentation procedure under the assumption of the multivariate Gaussian mixture. Jensen-Shannon divergence between two multivariate Gaussian distributions is emp...
Estimating Extremal Dependence in Univariate and Multivariate Time Series via the Extremogram
Extremogram extremal dependence stationary bootstrap financial time series
2011/10/9
Abstract: Davis and Mikosch [7] introduced the extremogram as a flexible quantitative tool for measuring various types of extremal dependence in a stationary time series. There we showed some standard...
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...
Comparative application of artificial neural networks and genetic algorithms for multivariate time-series modelling of algal blooms in freshwater lakes
Comparative application artificial neural networks genetic algorithms time-series modelling
2009/12/4
The paper compares potentials and achievements of artificial neural networks and genetic algorithms in terms of forecasting and understanding of algal blooms in Lake Kasumigaura (Japan). Despite the c...
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...
A One-Factor Multivariate Time Series Model of Metropolitan Wage Rates
State space model Dynamic factor anal-ysis Kalman filter Method of scoring Unobserved com- ponent estimation
2014/3/18
The paper formulates and estimates a single-factor multi-variate time series model. The model is a dynamic gen-eralization of the multiple indicator (or factor analysis) model. ...