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Mathematical Models for Dynamics of Molecular Processes in Living Biological Cells. A Single Particle Tracking Approach

open access: yesAnnales Mathematicae Silesianae, 2018
In this survey paper we present a systematic methodology of how to identify origins of fractional dynamics. We consider three models leading to it, namely fractional Brownian motion (FBM), fractional Lévy stable motion (FLSM) and autoregressive ...
Weron Aleksander
doaj   +1 more source

Growth enterprise market in Hong Kong: Efficiency evolution and long memory in return and volatility [PDF]

open access: yesJournal of Asian Business and Economic Studies, 2020
Purpose – Growth enterprise market (GEM) in Hong Kong is acknowledged as one of the world’s most successful examples of small and medium enterprise (SME) stock market.
Trang Nguyen   +3 more
doaj   +1 more source

Combining long memory and level shifts in modeling and forecasting the volatility of asset returns [PDF]

open access: yes, 2017
We propose a parametric state space model of asset return volatility with an accompanying estimation and forecasting framework that allows for ARFIMA dynamics, random level shifts and measurement errors.
Perron, Pierre, Varneskov, Rasmus T.
core   +1 more source

Estimation of parameters of autoregressive models with fractional differences in the presence of additive noise

open access: yesВестник Самарского университета: Естественнонаучная серия, 2023
For modeling in time series, models with fractional differences are widely used. The best known model is the ARFIMA (autoregressive fractionally integrated moving average) model.
Dmitriy V. Ivanov
doaj   +1 more source

Wavelet based deseasonalization for modelling and forecasting of daily discharge series considering long range dependence

open access: yesJournal of Hydrology and Hydromechanics, 2014
Short term streamflow forecasting is important for operational control and risk management in hydrology. Despite a wide range of models available, the impact of long range dependence is often neglected when considering short term forecasting.
Szolgayová Elena   +3 more
doaj   +1 more source

MODELLING FOR THE WAVELET COEFFICIENTS OF ARFIMA PROCESSES [PDF]

open access: yesJournal of Time Series Analysis, 2014
AbstractWe consider a model for the discrete nonboundary wavelet coefficients of autoregressive fractionally integrated moving average (ARFIMA) processes in each scale. Because the utility of the wavelet transform for the long‐range dependent processes, which many authors have explained in semi‐parametrical literature, is approximating the transformed ...
openaire   +1 more source

Forecasting Diabetes Patients Attendance at Al-Baha Hospitals Using Autoregressive Fractional Integrated Moving Average (ARFIMA) Models

open access: yes, 2020
Diabetes has become a concern in the developed and developing countries with its growing number of patients reported to the ministry of health records.
S. Zahrani   +3 more
semanticscholar   +1 more source

Forecasting energy futures volatility based on the unbiased extreme value volatility estimator

open access: yesIIMB Management Review, 2017
This paper uses the opening, high, low, and closing prices of five energy futures to estimate and model volatility based on the unbiased extreme value volatility estimator (the Add RS estimator).
Dilip Kumar
doaj   +1 more source

ARFIMA Model for Short Term Forecasting of New Death Cases COVID-19 [PDF]

open access: yesE3S Web of Conferences, 2020
COVID-19 is an infectious disease that can spread from one person to another and has a high potential for death. The infection of COVID-19 is spreading massive and fast that causes the extreme fluctuating data spread and long memory effects.
Kartikasari Puspita   +2 more
doaj   +1 more source

Modelling exchange rate volatility with random level shifts [PDF]

open access: yes, 2016
Recent literature has shown that the volatility of exchange rate returns displays long memory features. It has also been shown that if a short memory process is contaminated by level shifts, the estimate of the long memory parameter tends to be upward ...
Li, Ye, Perron, Pierre, Xu, Jiawen
core   +1 more source

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