… needed in the usual autoregressive-moving average model. In these circumstances the
fractional autoregressive-moving average model may possibly produce a more parsimonious fit. …

… time series model for velocity data in turbulent flows. The new model employs tempered
fractional … 0, α, λ, 0) time series model can be viewed as a discrete time version of Yaglom noise. …

… It has become standard practice for time series analysts to consider differencing their series
'… achieve a form of the series that can be identified as an ARMA model. If a series does need …

… We consider the estimation of parametric fractional time series models in which not only is …
in case of a quite general univariate model. For a multivariate model, we establish asymptotic …

… The value M- 30 was used for all the model fitting described in this paper, since an AR(30)
model seems sufficient to account for most of the dependence observed in hydrological and …

… In the next section, we present the fractional models and in Section 3 our main results. In
Section 4, we give an application of our theoretical results to a data set of Gallup opinion polls. …

… a time-series. We employ fractionally integrated dual memory models and compare results to
classical time-series models in … models offer better representation of the original time-series …

… that requires stochastic models in describing its pattern. This paper therefore suggests
seasonal time series models in predicting rainfall in Nigeria. The two models considered are the …

… different class of fractional vector autoregressive models that is not nested with the multivariate
fractional time-series models in this article. Dealing with the same model setup as the …

… of fractal time series are explained in Section 3. Some models of fractal time series are …
consider a widely used fractal time series called the fractional Brownian motion fBm introduced by …