Results 91 to 100 of about 4,760 (217)
Malaysian Journal of Computing, 2020 The main purpose of this study is to compare the performances of univariate and bivariate models on four-time series variables of the crude palm oil industry in Peninsular Malaysia.
Pauline Jin Wee Mah, Nur Nadhirah Nanyan
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Motricidade, 2013 This study aimed to find parameters to characterize heart rate variability (HRV) and discriminate healthy subjects and patients with heart diseases. The parameters used for discrimination characterize the different components of HRV memory (short and ...
Argentina Leite +2 more
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Vietnam Journal of Science, Technology and EngineeringThis study establishes the efficiency of the maintenance workforce in a process plant, utilising combined models, including artificial neural networks (ANN)-weighted aggregated sum product assessment (WASPAS) and ANN-fuzzy inference system (FIS)-WASPAS.
Sunday Ayoola Oke +1 more
doaj
An Overview of FIGARCH and Related Time Series Models
Austrian Journal of Statistics, 2016 This paper reviews the theory and applications related to fractionally integrated generalized autoregressive conditional heteroscedastic (FIGARCH) models, mainly for describing the observed persistence in the volatility of a time series.
Maryam Tayefi, T.V. Ramanathan
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Modeling of nonstationarity and long memory with RS-ARFIMA-GARCH model
African Journal of Applied Statistics, 2018 We consider in this study the problem of confusion between the nonstationarity and the long memory. Many authors have pointed out, in empirical case, the existence of long memory in financial and economics time series, through processes supposed short memory stationary (See Mikosch and Stáricá (2004) and Lobato and Savin (1998)).
FOFANA, Souleymane +2 more
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Long Memory Features in Return and Volatility of the Malaysian Stock Market [PDF]
This study aims to investigate the existence of long memory in the Malaysian stock market utilizing daily stock price index from the period 1998:09 to 2009:12.
Mohammad Tariqul Islam Khan +1 more
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Inference and Forecasting for ARFIMA Models With an Application to US and UK Inflation
Studies in Nonlinear Dynamics & Econometrics, 2004 Practical aspects of likelihood-based inference and forecasting of series with long memory are considered, based on the arfima(p; d; q) model with deterministic regressors. Sampling characteristics of approximate and exact first-order asymptotic methods are compared. The analysis is extended using modified profile likelihood analysis, which is a higher-
Doornik, J, Ooms, M
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Combining long memory and level shifts in modeling and forecasting the volatility of asset returns [PDF]
, 2015 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.
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Investigating Inflation Dynamics and Structural Change with an Adaptive ARFIMA Approach [PDF]
Previous models of monthly CPI inflation time series have focused on possible regime shifts, non-linearities and the feature of long memory. This paper proposes a new time series model, named Adaptive ARFIMA; which appears well suited to describe ...
Claudio Morana, Richard T. Baille
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Prediction intervals in the ARFIMA model using bootstrap G
Financial Statistical Journal, 2018 This paper presents a bootstrap resampling scheme to build pre-diction intervals for future values in fractionally autoregressive movingaverage (ARFIMA) models. Standard techniques to calculate forecastintervals rely on the assumption of normality of the data and do nottake into account the uncertainty associated with parameter estima-tion.
Glaura C. Franco +2 more
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