Distance measures between a reference signal and the autoregressive estimate are used as an objective reference for comparing autoregressive order-determining criteria. The distance measures discussed are the rms log spectral deviation, its normalized cepstral distance approximation, the normalized autoregressive transfer function error, the equivalent Itakura distance, and the average squared ...

In this note we consider the autoregressive moving average recurrent neural network ARMA-NN(1; 1) process. We show that in contrast to the pure autoregressive process simple ARMA-NN processes exist which are not irreducible. We prove that the controllability of the linear part of the process is sufficient for irreducibility. For the irreducible process essentially the shortcut weight correspond...

Burg estimators are classically used for the estimation of the autocovariance of a stationary autoregressive process. We propose to consider scale mixtures of stationary autoregressive processes, a non-Gaussian extension of the latter. The traces of such processes are Spherically Invariant Random Vectors (SIRV) with a constraint on the scatter matrix due to the autoregressive model. We propose ...

This paper reviews the analysis of the threshold autoregressive, smooth threshold autoregressive, and Markov switching autoregressive models from the Bayesian perspective. For each model we start by describing a baseline model and discussing possible extensions and applications. Then we review the choice of prior, inference, tests against the linear hypothesis, and conclude with models selectio...

in recent years, various time series models have been proposed for financial markets forecasting. in each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. many researchers have compared different time series models together in order to determine more efficient ...

Abelian group, 278 acceptance probability, 24, 37, 246 adaptive Metropolis algorithm, 33 Akaike’s information criterion, 209, 320 alpha-beta recursion, 11, 390 annealed importance sampling, 321 aperiodic chain, 23 APF, see auxiliary particle filter AR model, see autoregressive model assumed density filtering, 17, 143, 388 autoregressive hidden Markov model, 185 autoregressive model, 4, 9, 111 a...

The vector autoregressive model is very popular for modeling multiple time series. Estimation of its parameters is typically done by a least squares procedure. However, this estimation method is unreliable when outliers are present in the data, and therefore we propose to estimate the vector autoregressive model by using a multivariate least trimmed squares estimator. We also show how the order...

In this paper a nonparametric neural network (NN) technique for prediction of future values of a signal based on its past history is presented. This approach bypasses modeling, identification, and parameter estimation phases that are required by conventional parametric techniques. A multi-layer feed forward NN is employed. It develops an internal model of the signal through a training operation...