Results 261 to 270 of about 8,933,709 (332)

A numerical approach to overcome the very-low Reynolds number limitation of the artificial compressibility for incompressible flows. [PDF]

Heliyon
Monokrousos N, Könözsy L, Pachidis V, Sozio E, Rossi F.   +4 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

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.
Andrew Sutcliffe
semanticscholar   +2 more sources

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.
C. Tseng
semanticscholar   +2 more sources

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.
C. Granger, Roselyne Joyeux
semanticscholar   +3 more sources

FRACTIONAL DIFFERENCING MODELING IN HYDROLOGY

JAWRA Journal of the American Water Resources Association, 1985
ABSTRACT: Fractional differencing is a tool for modeling time series which have long‐term dependence; i.e., series in which the correlation between distant observations, though small, is not negligible. Fractionally differenced ARIMA models are formed by permitting the differencing parameter d in the familiar Box‐Jenkins ARIMA(p, d, q) models to take ...
J. Hosking
semanticscholar   +2 more sources

Modeling persistence in hydrological time series using fractional differencing

Water Resources Research, 1984
The class of autoregressive integrated moving average (ARIMA) time series models may be generalized by permitting the degree of differencing d to take fractional values. Models including fractional differencing are capable of representing persistent series (d > 0) or short‐memory series (d = 0).
J. Hosking
semanticscholar   +2 more sources

Identification of fractional differencing autoregressive models

Communications in Statistics - Theory and Methods, 1995
This paper proposes an identification method to fractional differencing autoregressive models, and this method gives a consistent estimator for fractional differencing order and efficient estimates for parameters in fractional differencing autoregressive models.
Guibin Li
semanticscholar   +2 more sources

Empirical study of ARFIMA model based on fractional differencing

Physica A: Statistical Mechanics and its Applications, 2007
Abstract In this paper, we studied the long-term memory of Hong Kong Hang Sheng index using MRS analysis, established ARFIMA model for it, and detailed the procedure of fractional differencing. Furthermore, we compared the ARFIMA model built by this means with the one that took first-order differencing as an alternative.
Jin Xiu, Yao Jin
semanticscholar   +2 more sources

Editors' introduction: Fractional differencing and long memory processes

Journal of Econometrics, 1996
R. Baillie, M. King
semanticscholar   +2 more sources

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
openaire   +2 more sources