The tracking of nonstationary EEG with time-varying ARMA models is discussed. A method for detecting spindles in rat EEG is presented. The method is based on tracking of a single system pole of the ARMA model.

We consider computationally-fast methods for estimating parameters in ARMA processes from binary time series data, obtained by thresholding the latent ARMA process. All methods involve matching estimated and expected autocorrelations of the binary series. In particular, we focus on the spectral representation of the likelihood of an ARMA process and derive a restricted form of this likelihood, ...

Recent work has proposed a certainty trend (CT) elimination technique employed for the auto-regressive/autoregressive and moving-average (AR/ARMA) model pulse position prediction. In this paper, we investigate the intra pulse parameter estimation and pulse position prediction of the chirp and stochastic pulse position modulation (CSPPM) combined signal. The quick dechirp method is adopted to th...

ARFIMA is a time series forecasting model, which is an improve d ARMA model, the ARFIMA model proposed in this article is d emonstrated and deduced in detail. combined with network traffi c of CERNET backbone and the ARFIMA model,the result sho ws that,compare to the ARMA model, the prediction efficiency a nd accuracy has increased significantly, and not susceptible to sa mpling.

Time-series Autoregressive Moving Average (ARMA) models were employed to model tree crown profiles for two California hardwood species (blue oak and interior live oak). There are three major components of these models: a polynomial trend, an ARMA model, and unaccounted for variation. The polynomial trend was used to achieve a stationary series. For these crown profiles, the use of a quadratic t...

There have been a lot of works relating to time series analysis. In this paper, the Bayesian analysis method for ARMA model is discussed and an application example is given. Firstly, the Bayesian theoretic results about AR model and the determination approach for model order are obtained. Then, the approach are presented for Bayesian analysis of MA and ARMA models. As its application, the forec...

Let Xt be an /-dimensional ARMA (p, q) process. Let g: U l -> W be a measurable function. Define a process Zt by Zt = g(Xt). Generally, Z.is not an ARMA process. However, we are interested in such functions g, for which Zt is also an AR process. It is important to know the orders of the process Zt. In the most cases we find only some bounds for them. From the practical point of view, our consid...

This paper investigates the asymptotic theory for a vector autoregressive moving average–generalized autoregressive conditional heteroskedasticity ~ARMAGARCH! model+ The conditions for the strict stationarity, the ergodicity, and the higher order moments of the model are established+ Consistency of the quasimaximum-likelihood estimator ~QMLE! is proved under only the second-order moment conditi...

There is a great demand for statist ical modeling of phenomena tha t evolve in bo th space and time. Practical examples are those in Haslett and Raf tery (1989), Handcock and Wallis (1994), Cressie and Huang (1999), Brix and Diggle (2001), Stroud et al. (2001), De Iaco et al. (2002), Gneit ing (2002), and Hartfield and Gunst (2003), to mention but a few. Two commonly used tools to describe the ...

In this paper, the S&P 500 stock index is studied for its time varying volatility and stylized facts. The ARMA mean equation with asymmetric power ARCH errors is used to model the series correlations and the conditional heteroscadesticity in the asset returns. The conditional distributions of the standardized residuals are assumed to be the normal distribution, the t distribution or the skew-t ...