Six Poisson autoregressive models of order p [PAR(p)] of daily wildland arson ignition counts are estimated for five locations in Florida (1994–2001). In addition, a fixed effects time-series Poisson model of annual arson counts is estimated for all Florida counties (1995–2001). PAR(p) model estimates reveal highly significant arson ignition autocorrelation, lasting up to eleven days, in additi...

A nonlinear autoregressive model, the process feedback nonlinear autoregressive (PFNAR) model, in which the autoregressive coe0cients are a function of the combination of past data, is proposed. The autoregressive coe0cients of the PFNAR model consist of sequential autoregressive parts, and a data process feedback part that feeds back the in2uence from previous data points with “signi4cant dela...

Abstract. Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average) models for seasonal streamflow series). However, with McLeod-Li test and Engle’s Lagrange Multiplier test, clear evidences are found for t...

We give a recursive algorithm for the computation of the first and second order derivatives of the entropy of a periodic autoregressive process with respect to the autocovariances. It is an extentions of the periodic Levinson-Durbin algorithm. The algorithm has been developed for use at one of the steps of an entropy maximisation method developed by the authors. We give numerical examples of en...

One gives a recursive algorithm for the computation of the first and second order derivatives of the entropy of a periodic autoregressive process with respect to the autocovariances. It is an extension of the periodic LevinsonDurbin algorithm. The algorithm has been developed for use at one of the steps of an entropy maximization method developed by the authors. Numerical examples of entropy ma...

The purpose of this study is to contrast the forecasting performance of two non-linear models, a regime-switching vector autoregressive model (RS-VAR) and a recurrent neural network (RNN), to that of a linear benchmark VAR model. Our specific forecasting experiment is UK inflation and we utilize monthly data from 1969-2003. The RS-VAR and the RNN perform approximately on par over both monthly a...

Abstract The periodic behavior of real data can be manifested in the time series or its characteristics. One characteristics that often manifests is sample autocovariance function. In this case, periodically correlated (PC) considered. main models exhibits PC property autoregressive (PARMA) model considered as generalization classical moving average (ARMA) process. However, when one considers d...

(1-2) Pj(t) = Pj(t + d) , qt = qt+d and {E,} is a normal white noise with zero mean value and a variance a. Such process is natural analogy of the periodic autoregressive process (see e.g. [ l ] , [5], [6], [7]) and therefore the idea originates to use the estimation technique described in [3] for the treatment of the multiple moving average models (the same approach to the multiple autoregress...