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Recurrence analysis techniques for non-stationary and non-linear data [PDF]

Kostov, Phillip, Lingard, John
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ON PREDICTION WITH FRACTIONALLY DIFFERENCED ARIMA MODELS

Journal of Time Series Analysis, 1988
Abstract. This paper considers some extended results associated with the predictors of long‐memory time series models. These direct methods of obtaining predictors of fractionally differenced autoregressive integrated moving‐average (ARIMA) processes have advantages from the theoretical point of view.
Peiris, M. S, Perera, B. J. C
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Time‐series forecasting using fractional differencing

Journal of Forecasting, 1994
AbstractThe main failure of ARIMA modelling as used in practice are the limiting constraints imposed by differencing to achieve stationarity. The use of fractional differencing opens up a much wider and realistic behaviour for the trend and seasonal components than traditional integer differencing.
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Design of fractional Hilbert transformer using fractional differencing and different windows

2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon), 2017
In this paper, first the fractional Hilbert transformer and window functions are defined along with their properties and then the existing design based on fractional differencing filter is improved by using different window functions. Then the comparison of the phase responses are shown between the existing design and the proposed design using ...
Sushil Kumar, Dharmendra K. Upadhyay
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Analytical design of fractional Hilbert transformer using fractional differencing

Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03., 2003
Conventionally, fractional differencing (FD) has been successfully used to generate a fractal process called fractional Brownian motion, and the fractional Hilbert transformer (FHT) has been also applied to the edge detection of images and the construction of secure single-side band (SSB) communication systems.
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On the Invertibility of Fractionally Differenced ARIMA Processes

Biometrika, 1993
Summary: To evaluate how the condition for invertibility of fractional ARIMA \((p,d,q)\) processes can be achieved, we formulate a measure based on the prediction error related to the autoregressive inversion. Some results are obtained by investigating the behaviour of the measure.
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Fractionally Differenced and Fractionally Integrated Processes

2016
The adjective “fractional” appears frequently in the names of processes related to long-range dependence; two immediate examples are the fractional Brownian motion of Example 3.5.1 and the fractional Gaussian noise introduced in Section 5 ...
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Fractionally differenced models for water quality time series

Annals of Operations Research, 1987
This paper deals with the selection and evaluation of statistical techniques for use in the modeling and forecasting of water quality time series. The focus is on statistical concepts relevant to the analysis of flows and concentrations. A selection of time series procedures has been used for auditing water quality archival data, including the ...
W. Smith, C. M. Harris
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An Introduction to Long-Memory Time Series Models and Fractional Differencing

Journal of Time Series Analysis, 1980
Abstract It has become standard practice for time series analysts to consider differencing their series ‘to achieve stationarity’. By this they mean that one differences to achieve a form of the series that can be identified as an ARMA model.
Granger, C. W. J., Joyeux, Roselyne
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Parameter estimation in low order fractionally differenced ARMA processes

Stochastic Hydrology and Hydraulics, 1989
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Boes, D. C., Davis, R. A., Gupta, S. N.
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