Results 141 to 150 of about 467 (180)
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A Novel Prediction Method for ARFIMA Processes
2011 International Conference on Computational and Information Sciences, 2011The 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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Network Anomaly Detection Based on ARFIMA Model
2015In this paper, the estimation model ARFIMA is presented as a method of detecting anomalies in network traffic. Parameters estimation and model identification are performed with the use of algorithms of: Geweke and Porter-Hudak (estimation of the differencing parameters) and Box-Jankins (identification of the row of the model).
Tomasz Andrysiak, Lukasz Saganowski
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Indirect Inference for ARFIMA Processes
IFAC Proceedings Volumes, 1998Abstract Inference for ARFIMA processes is more complicated than that for ordinary ARMA models. This has limited the application of these processes in the practice of modelling economic time series. In this paper, a procedure of indirect estimation for fractionally integrated processes is proposed and discussed.
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Preliminary estimation of ARFIMA models
2000In this article we propose a preliminary estimator for the parameters of an ARFIMA(p,d,q) model. The estimation procedure is based on the search of the element in the class of ARFIMA models closest to the estimated ARMA model which best fits the observed time series.
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Indirect estimation of ARFIMA and VARFIMA models
Journal of Econometrics, 1999zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Martin, Vance L., Wilkins, Nigel P.
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Bayesian prediction for vector ARFIMA processes
International Journal of Forecasting, 2002Abstract We provide explicit formulae for the joint predictive distribution of a Gaussian vector autoregressive fractionally integrated moving average (VARFIMA) process and describe a Bayesian method for its feasible evaluation. Inference for the parameters in the Bayesian framework is based on the joint posterior distribution of the model parameters
Nalini Ravishanker, Bonnie K. Ray
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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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Arfima-Figarch vs. Arfima-Hygarch: Case Study ETF Returns of Emerging Asian Countries
Asian Journal of Finance & Accounting, 2014This research investigate the long memory returns for ETF returns index of seven Asian countries in Emerging Markets Equities during 2008-2013 periods. Those ETFs are Wisdom Tree Indian Rupee Fund (ICN), Market Vectors Indonesia Index (IDX), iShares MSCI Malaysia Index Fund (EWM), Market Vectors Russia ETF (RSX), and iShares MSCI Thailand Investable ...
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Parametric estimation for ARFIMA models via spectral methods
Statistical Methods & Applications, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
COLI, Mauro +2 more
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Long-Range Dependence and ARFIMA Models
2013In 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 +2 more
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