Ten candidate models of the Auto-Regressive Moving Average (ARMA) family are investigated for representing and forecasting monthly and ten-day streamflow in three Indian rivers. The best models for forecasting and representation of data are selected by using the criteria of Minimum Mean Square Error (MMSE) and Maximum Likelihood (ML) respectively. The selected models are validated for significa...

We show how the ARMA-Power GARCH model for the conditional mean and variance can be adapted to analyze times series data showing asymmetry. Dynamics is introduced in the location and the dispersion parameters of skewed location-scale distributions using the same type of structure found in the conditional mean and in the conditional variance in the ARMA-APARCH model. We also propose a general dy...

In this article, we propose an innovative way of estimating pitch from speech waveform data, using an iterative ARMA technique that efficiently estimates multiple frequency components of a time series. Additionally, the harmonic structure of voiced speech and the smoothness of its pitch period are incorporated into the iterative ARMA technique, and this novel integration results in an efficient...

We present a technique for simulating time-varying mobile radio channels. This technique is specifically suited to the small relative Doppler bandwidths of wideband channels encountered in CDMA and OFDM communications. A “subsampled” ARMA innovations filter and multistage interpolation are used to achieve an accurate and computationally efficient approximation of specified or measured Doppler s...

We address the problem of estimating the motion of a wide-band source from single passive sensor measurements, for example, estimation of the speed and position of a car moving on a road from the recording of its acoustic signature at a microphone located next to the road. We present a new computationally efficient method based on a time-varying ARMA model for Doppler-shifted random processes. ...

A procedure is proposed for computing the autocovariances and the ARMA representations of the squares, and higher-order powers, of Markov-switching GARCH models. It is shown that many interesting subclasses of the general model can be discriminated in view of their autocovariance structures. Explicit derivation of the autocovariances allows for parameter estimation in the general model, via a G...

The difference between training and testing environments is the major reason of performance degradation of speech recognition. In this paper, to further decrease the mismatch, we apply temporal filtering, Auto-Regression and Moving-Average (ARMA) filtering or RelAtive SpecTrAl (RASTA) filtering, as a post-processor for the log-Energy dynamic Range Normalization-Cepstral Mean and Variance Normal...

Given observations on a stationary economie vector time series process we show that the best % periods ahead forecast (best in the sense of having minimal forecast error variance) of one of the variables can be consistently estimated by nonparametric regression on an ARMA memory index. Our approach is based on a combination of the ARMA memory index modeling approach of Bierens (1986a) with a mo...

We consider maximum likelihood estimation of a particular noninvertible ARMA model with autoregressive conditionally heteroskedastic (ARCH) errors. The model can be seen as an extension to so-called all-pass models in that it allows for autocorrelation and for more flexible forms of conditional heteroskedasticity. These features may be attractive especially in economic and financial application...

Nonstationary ARIMA processes and nearly nonstationary ARMA processes, such as autoregressive processes having a root of the AR polynomial close to the unit circle, have sample autocovariance and spectral properties that are, in practice, almost indistinguishable from those of a stationary longmemory process, such as a Fractionally Integrated ARMA (ARFIMA) process. Because of this, model misspe...