Results 161 to 170 of about 13,793,172 (210)
Comparative study on sea-level rise in the Yangtze River Estuary and Red River Estuary in the recent 30 years. [PDF]
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Another look at the forecast performance of ARFIMA models
International Review of Financial Analysis, 2004This paper investigates the out-of-sample forecast performance of the autoregressive fractionally integrated moving average [ARFIMA (0,d,0)] specification, both when the underlying value of the fractional differencing parameter (d) is known a priori and when it is unknown.
Craig Ellis
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Optimal prediction with nonstationary ARFIMA model
Journal of Forecasting, 2007AbstractWe propose two methods to predict nonstationary long‐memory time series. In the first one we estimate the long‐range dependent parameterdby using tapered data; we then take the nonstationary fractional filter to obtain stationary and short‐memory time series.
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Modeling of water usage by means of ARFIMA–GARCH processes
Physica A: Statistical Mechanics and Its Applications, 2018Abstract This paper addresses an important problem of modeling and prediction of phenomena with antipersistent behavior and variance changing in time. As a proper stochastic model we propose an autoregressive fractionally integrated moving average (ARFIMA) process with generalized autoregressive conditional heteroskedasticity (GARCH) noise. First, we
Janusz Gajda +2 more
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Mathematics and Computers in Simulation, 2002 In this paper, we examine the finite-sample properties of the approximate maximum likelihood estimate (MLE) of the fractional differencing parameter d in an ARFIMA(p, d, q) model based on the wavelet coefficients.
Y K Tse, V V Anh
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ARFIMA model decomposition for hydrological time series
, 2004 In questo lavoro viene proposta una procedura statistica per la decomposizione di processi ARFIMA(p,d,q) in componenti elementari. Il problema viene ricondotto a quello della decomposizione di un processo ARMA(p+1,q+1) sfruttando il modello ARMA(1,1) come ’proxy’ della componente di lunga memoria.
CORDUAS, MARCELLA, PICCOLO, DOMENICO
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Bayesian Inference for ARFIMA Models
Journal of Time Series Analysis, 2019This article develops practical methods for Bayesian inference in the autoregressive fractionally integrated moving average (ARFIMA) model using the exact likelihood function, any proper prior distribution, and time series that may have thousands of observations.
Durham, Garland +3 more
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Bayesian model selection in ARFIMA models
Expert Systems with Applications, 2010Various model selection criteria such as Akaike information criterion (AIC; Akaike, 1973), Bayesian information criterion (BIC; Akaike, 1979) and Hannan-Quinn criterion (HQC; Hannan, 1980) are used for model specification in autoregressive fractional integrated moving average (ARFIMA) models. Classical model selection criteria require to calculate both
Erol Egrioglu, Süleyman Günay
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Adaptive ARFIMA models with applications to inflation
Economic Modelling, 2012Abstract Many previous analyses of inflation have used either long memory or nonlinear time series models. This paper suggests a simple adaptive modification of the basic ARFIMA model, which uses a flexible Fourier form to allow for a time varying intercept.
Morana, C, Baillie, RT
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Invariance of the first difference in ARFIMA models
Computational Statistics, 2006The main goal of the paper is to analyze which estimation method for the fractional parameter is invariant to first-differencing when the model is described by an ARFIMA(p,d,q) process. The authors consider the performance of four estimation methods, belonging to parametric and semiparametric classes, for non-stationary ARFIMA models with main interest
Barbara P. Olbermann +2 more
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