Results 191 to 200 of about 21,636,733 (224)

Modelling long-term heart rate variability: an ARFIMA approach

Biomedizinische Technik/Biomedical Engineering, 2006
Long-term heart rate variability (HRV) series can be described by time-variant autoregressive modelling. HRV recordings show dependence between distant observations that is not negligible, suggesting the existence of long-range correlations. In this work, selective adaptive segmentation combined with fractionally integrated autoregressive moving ...
Argentina S, Leite, Ana Paula, Rocha, M Eduarda, Silva, Ovídio, Costa   +3 more
openaire   +2 more sources

Comparing ARFIMA and ARIMA Models in Forecasting under Five Mortality Rate in Tanzania

Asian Journal of Probability and Statistics
Tanzania has been taking various measures to drop the Under-Five Mortality Rate (UFMR), but the pace to meet national and global UFMR targets has been slow.
Sadock Aron Mwijalilege, Michael L. Kadigi, Castory Kibiki   +2 more
semanticscholar   +1 more source

Exploring Long-memory Dynamics in Nigerian Commercial Banks' Lending Rates: A Comparative Analysis of ARIMA, ARFIMA, and FIGARCH Models

Asian Journal of Probability and Statistics
This study investigates the dynamics of commercial banks’ maximum lending rates in Nigeria using short-memory ARIMA and long-memory models such as ARFIMA and the FIGARCH models. The data for the study spanned from January 1997 to May 2024.
G. L. Tuaneh, Z. D. Deebom, Vincent Mark Akah   +2 more
semanticscholar   +1 more source

On the Efficacy of ARFIMA, ARTFIMA, and MARFIMA Models in Forecasting Nigerian Crude Oil Prices

UMYU Scientifica
This study presents a comprehensive evaluation of three advanced long-memory time series models— the Autoregressive Fractionally Integrated Moving Average (ARFIMA), the Autoregressive Tempered Fractionally Integrated Moving Average (ARTFIMA), and the ...
M. Tasi'u, A. Bello, H. Garba, Baba Alhaji Bukar   +3 more
semanticscholar   +1 more source

Network Anomaly Detection Based on ARFIMA Model

2015
In 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, Łukasz Saganowski
openaire   +1 more source

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
semanticscholar   +1 more source

On the estimation and diagnostic checking of the ARFIMA–HYGARCH model

Computational Statistics & Data Analysis, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kwan, W, Li, WK, Li, G
openaire   +3 more sources

Modeling and predicting stock returns using the ARFIMA-FIGARCH

2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009
Modeling of real world financial time series such as stock returns are very difficult, because of their inherent characteristics. ARIMA and GARCH models are frequently used in such cases. It is proven of late that, the traditional models may not produce the best results. Lot of recent literature says the successes of hybrid models.
P. Bagavathi Sivakumar, V. P. Mohandas
openaire   +1 more source

Monitoring long‐memory air quality data using ARFIMA model

Environmetrics, 2007
AbstractStatistical control chart is commonly used in the industry to help ensure stability of manufacturing process and it can also be used to monitor the environmental data, such as industrial waste or effluent of manufacturing process. However, control chart needs to be modified if the set of environmental data exhibits the property of long memory ...
Jeh‐Nan Pan, Su‐Tsu Chen
openaire   +1 more source

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
openaire   +1 more source