Results 201 to 210 of about 7,382 (224)

Optimal prediction with nonstationary ARFIMA model

Journal of Forecasting, 2007
AbstractWe 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 PMU Data Using ARFIMA Models

2018 Clemson University Power Systems Conference (PSC), 2018
Installing Phasor Measurement Units (PMUs) in the smart grid has played an important role in having more reliable and secure grid. Due to the high sampling rate (50 samples/s), PMU generates massive amount of data compared to the conventional SCADA system.
Laith Shalalfeh, Paul Bogdan, Edmond Jonckheere   +2 more
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Statistical analysis of DWT coefficients of fGn processes using ARFIMA(p,d,q) models

, 2020
Fractional Gaussian noise (fGn) provides an important parametric representation for the data recorded from long-memory processes. Also it has been well established in literature that the orthogonal wavelet transforms prove to be the optimal bases to ...
Shivam Bhardwaj, V. Gadre, E. Chandrasekhar   +2 more
semanticscholar   +1 more source

Long-Range Dependence and ARFIMA Models

2013
In this chapter, long-range dependence concept, Hurst phenomenon and ARFIMA models are introduced and the earlier work on these subjects are reviewed. Several methodologies are introduced for the estimation of long-range dependence index (Hurst number or fractional difference parameter).
Ali Ercan, M. Levent Kavvas, Rovshan K. Abbasov   +2 more
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Analysing inflation by the fractionally integrated ARFIMA-GARCH model

Journal of Applied Econometrics, 1996
This paper considers the application of long-memory processes to describing inflation for 10 countries. We implement a new procedure to obtain approximate maximum likelihood estimates of an ARFIMA-GARCH process; which is fractionally integrated I(d) with a superimposed stationary ARMA component in its conditional mean.
Baillie, Richard T, Chung, Ching-Fan, Tieslau, Margie A   +2 more
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A Novel Prediction Method for ARFIMA Processes

2011 International Conference on Computational and Information Sciences, 2011
The class of autoregressive fractionally integrated moving average (ARFIMA) model is an important type of long memory processes which are widely used in many fields. In this paper, a novel nonparametric method is proposed to predict ARFIMA processes based on phase space reconstruction theory and multivariate local linear estimator.
Wangyong Lv, Huiqi Wang
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DIFFERENTIAL GEOMETRY OFARFIMAPROCESSES

Communications in Statistics - Theory and Methods, 2001
Autoregressive fractionally integrated moving average (ARFIMA) processes are widely used for modeling time series exhibiting both long-memory and short-memory behavior. Properties of Toeplitz matrices associated with the spectral density functions of Gaussian ARFIMAprocesses are used to compute differential geometric quantities.
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Bayesian estimation of fractional difference parameter in ARFIMA models and its application

Information Sciences, 2023
Masoud Fazlalipour Miyandoab, P. Nasiri, A. M. Mosammam   +2 more
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A Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting

Computational Economics, 2022
H. Boubaker, G. Canarella, Rangan Gupta, S. Miller   +3 more
semanticscholar   +1 more source

ARFIMA modely časových řad

2014
The thesis deal with long-memory processes which are defined by several ways. The main concern is dedicated to ARFIMA model, to its basic properties and its application. Next, graphical, semiparametric and parametric estimation methods of ARFIMA parameters are described in detail.
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