Multivariate time series exhibit two types of dependence: across variables and points. Vine copulas are graphical models for the dependence can conveniently capture both in same model. We derive maximal class graph structures that guarantee stationarity under a natural verifiable condition called translation invariance. propose computationally efficient methods estimation, simulation, predictio...

Large tensor (multi-dimensional array) data routinely appear nowadays in a wide range of applications, due to modern collection capabilities. Often such observations are taken over time, forming time series. In this article we present factor model approach the analysis high-dimensional dynamic series and multi-category transport networks. This presents two estimation procedures along with their...

In this paper statistical and time series models are used for determining the random drift of a dynamically Tuned Gyroscope (DTG). This drift is compensated with optimal predictive transfer function. Also nonlinear neural-network and fuzzy-neural models are investigated for prediction and compensation of the random drift. Finally the different models are compared together and their advantages a...

This research was supported by the Army, Navy, Air Force and NASA under a contract. administered by the Office of Naval Research, with Yale University; by the National Science Foundation under Grant GU-2059 and the Air Force Office of Scientific Research under Contract AFOSR-68-l4l5 with the University of North Carolina at Chapel Hill. ** The major portion of the research for this paper was don...

In this paper, a comparison study is presented on artificial intelligence and time series models in 1-hour-ahead wind speed forecasting. Three types of typical neural networks, namely adaptive linear element, multilayer perceptrons, and radial basis function, and ARMA time series model are investigated. The wind speed data used are the hourly mean wind speed data collected at Binalood site in I...