Time series and dependent variables

Analysis of landings and environmental variables time series from the Caspian Sea

In the present study, the time series encompassing the years 1950-2011 of commercial landings from the Caspian Sea were analyzed using min/max autocorrelation factor analysis (MAFA) and dynamic factor analysis (DFA). These aim was to identify trends and explore the relationships between the response variables (annual landings of 10 species/groups such as kilkas, kutum, common carp, pike perch, ...

Fuzzy Time Series Model Incorporating Predictor Variables and Interval Partition

Prediction is a critical component in decision-making process for business management. Fuzzy Markov model is a common approach for dealing with the prediction of time series. However, not many studies devoted their attention to the effect of the parameters on model fitting for fuzzy Markov model. In the paper, we examine the prediction ability for fuzzy Markov model, based on the data of Taiwan...

Convergence of series of dependent φ-subgaussian random variables

The almost sure convergence of weighted sums of φ-subgaussian m-acceptable random variables is investigated. As corollaries, the main results are applied to the case of negatively dependent and m-dependent subgaussian random variables. Finally, an application to random Fourier series is presented. © 2007 Elsevier Inc. All rights reserved.

Central and Local Limit Theories in Markov Dependent Random Variables

Graphical Gaussian modelling of multivariate time series with latent variables

In time series analysis, inference about causeeffect relationships among multiple times series is commonly based on the concept of Granger causality, which exploits temporal structure to achieve causal ordering of dependent variables. One major problem in the application of Granger causality for the identification of causal relationships is the possible presence of latent variables that affect ...