Results 91 to 100 of about 23,794,745 (244)
WIREs Data Mining and Knowledge Discovery, Volume 14, Issue 1, January/February 2024.A review of recent research on the application of deep learning models to price forecast of financial time series, with information on model architectures, applications, advantages and disadvantages, and directions for future research. Abstract Accurately predicting the prices of financial time series is essential and challenging for the financial ...
Cheng Zhang +2 more
wiley +1 more source
Modeling and Forecasting the Volatility of the Nikkei 225 Realized Volatility Using the ARFIMA-GARCH Model [PDF]
In this paper, we apply the ARFIMA-GARCH model to the realized volatility and the continuous sample path variations constructed from high-frequency Nikkei 225 data.
Isao Ishida, Toshiaki Watanabe
core +3 more sources
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
doaj +1 more source
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
doaj +1 more source
Modelling and Forecasting Noisy Realized Volatility [PDF]
Several methods have recently been proposed in the ultra high frequency financial literature to remove the effects of microstructure noise and to obtain consistent estimates of the integrated volatility (IV) as a measure of ex-post daily volatility. Even
Asai, M., McAleer, M.J., Medeiros, M.
core +4 more sources
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
Measuring core inflation in the euro area [PDF]
We propose a measure of core inflation which is derived from a Markov switching ARFIMA model. The Markov switching ARFIMA model generalises the standard ARFIMA model allowing mean reversion to take place with respect to a changing unconditional mean.
Morana, Claudio
core
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
openaire +2 more sources
Sèries temporals amb memòria llarga: models ARFIMA
, 2023 Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2023 , Director: Josep Vives i Santa ...
openaire +1 more source
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
openaire +2 more sources