Multivariate time-series modeling and forecasting constitutes an important problem with numerous applications. In this work, we consider multivariate continuous time series modeling from aviation, where the data consists of multiple sensor measurements from real world flights. While traditional approaches such as VAR (vector auto-regressive) models have been widely used for aviation time series...

This paper investigates the information content of the ex post overnight return for one-day-ahead equity Value-at-Risk (VaR) forecasting. To do so, we deploy a univariate VaR modeling approach that constructs the forecast at market open and, accordingly, exploits the available overnight close-to-open price variation. The benchmark is the bivariate VaR modeling approach proposed by Ahoniemi et a...

1. To develop a state-of-the-art ocean 4-dimensional variational (4D-Var) data assimilation and ocean forecasting system for the Regional Ocean Modeling System (ROMS); 2. To develop a state-of-the-art suite of post-processing and diagnostic tools in support of ROMS 4D-Var; 3. To gain the necessary experience using the ROMS 4D-Var systems in complex circulation environments; 4. To train the next...

One of the most commonly used methods for modeling multivariate time series is the Vector Autoregressive Model (VAR). VAR is generally used to identify lead, lag and contemporaneous relationships describing Granger causality within and between time series. In this paper, we investigate VAR methodology for analyzing data consisting of multilayer time series which are spatially interdependent. Wh...

Vector autoregressions (VAR) are extensively used to model economic time series. The large number of parameters is the main diicult with VAR models, however. To overcome this, Litterman (1986) suggests to use a Bayesian strategy to estimate the VAR, equation by equation, where, a priori, the lags have decreasing importance (known as Litterman Prior). In this paper, a VAR model is analyzed throu...

Market risk exposure plays a key role for financial institutions risk management. A possible measure for this exposure is to evaluate losses likely to incur when the price of the portfolio’s assets declines using Value-at-Risk (VaR) estimates, one of the most prominent measure of financial downside market risk. This paper suggests an evolving possibilistic fuzzy modeling approach for VaR estima...

Multivariate time-series modeling and forecasting is an important problem with numerous applications. Traditional approaches such as VAR (vector auto-regressive) models and more recent approaches such as RNNs (recurrent neural networks) are indispensable tools in modeling time-series data. In many multivariate time series modeling problems, there is usually a significant linear dependency compo...

This paper proposes a Vector Autoregressive (VAR) model as a new technique for missing feature reconstruction in ASR. We model the spectral features using multiple VAR models. A VAR model predicts missing features as a linear function of a block of feature frames. We also propose two schemes for VAR training and testing. The experiments on AURORA-2 database have validated the modeling methodolo...

The Value-at-Risk (VAR) measurements are widely applied to estimate exposure to market risks. The traditional approaches to VAR computations the variance-covariance method, historical simulation, Monte Carlo simulation, and stress-testing do not provide satisfactory evaluation of possible losses. In this paper we review the recent advances in the VAR methodologies. The proposed improvements sti...

In financial market risk measurement, Value-at-Risk (VaR) techniques have proven to be a very useful and popular tool. Unfortunately, most VaR estimation models suffer from major drawbacks: the lognormal (Gaussian) modeling of the returns does not take into account the observed fat tail distribution and the non-stationarity of the financial instruments severely limits the efficiency of the VaR ...