South African inflation modelling using bootstrapped long short-term memory methods. [PDF]
SN Bus Econ, 2023 Kubheka S.
europepmc +1 more source
MODEL AUTOREGRESSIVE FRACTIONAL INTEGRATED MOVING AVERAGE (ARFIMA) DAN PENERAPANNYA
, 2010 Dalam analisis runtun waktu terdapat data yang memiliki ciri proses jangka pendek (short memory) dan data yang memiliki ciri proses jangka panjang (long memory).
Adyta Prabandoro Saputri
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Time Analysis of an Emergent Infection Spread Among Healthcare Workers: Lessons Learned from Early Wave of SARS-CoV-2. [PDF]
Int J Gen Med, 2022 Leme PAF +8 more
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Model-based stationarity filtering of long-term memory data applied to resting-state blood-oxygen-level-dependent signal. [PDF]
PLoS One, 2022 Bansal IR +4 more
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Temporal Structure in Sensorimotor Variability: A Stable Trait, But What For? [PDF]
Comput Brain Behav, 2023 Perquin MN +3 more
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ARFIMA model decomposition for hydrological time series
, 2004 In questo lavoro viene proposta una procedura statistica per la decomposizione di processi ARFIMA(p,d,q) in componenti elementari. Il problema viene ricondotto a quello della decomposizione di un processo ARMA(p+1,q+1) sfruttando il modello ARMA(1,1 ...
PICCOLO, DOMENICO, CORDUAS, MARCELLA
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, 2020 Model Autoregressive Fractionally Integrated Moving Average (ARFIMA) merupakan pengembangan dari model ARIMA dengan nilai differencing adalah bilangan pecahan.
Mohammad Jefrie Ilham Akbar, 4111415018
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Pandemic episodes, CO2 emissions and global temperatures. [PDF]
Theor Appl Climatol, 2022 Monge M, Gil-Alana LA.
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Forecasting commodity prices: empirical evidence using deep learning tools. [PDF]
Ann Oper Res, 2023 Ben Ameur H +4 more
europepmc +1 more source
Estimação do parâmetro "d " em modelos arfima
, 2000 Os modelos ARFIMA caracterizam-se por sua longa dependência e por possuírem o parâmetro d do modelo ARIMA (grau de diferenciação) assumindo valores fracionários. Quando no caso d Î (-0,5; 0,5), há estacionariedade.
Trevisan, Elma Suema +2 more
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