ARFIMA model applied to Malaysian stock market
Communications in Mathematical Biology and Neuroscience, 2022 openaire +1 more source
Forecasting the Romanian Unemployment Rate in Time of Health Crisis-A Univariate vs. Multivariate Time Series Approach. [PDF]
Int J Environ Res Public Health, 2021 Davidescu AA, Apostu SA, Marin A.
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Normalizing Logarithms Of Realized Volatility In An Arfima Model
, 2016 Modelling realized volatility with high-frequency returns is popular as it is an unbiased and efficient estimator of return volatility. A computationally simple model is fitting the logarithms of the realized volatilities with a fractionally integrated long-memory Gaussian process.
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Estimating Permutation Entropy Variability via Surrogate Time Series. [PDF]
Entropy (Basel), 2022 Ricci L, Perinelli A.
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Application of an ARFIMA Model to Estimate Hepatitis C Epidemics in Henan, China. [PDF]
Am J Trop Med HygWang Y, Liang Z, Qing S, Liu X, Xu C.
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Tracking progress towards Sustainable Development Goal 3.2 in Kenya using time series models. [PDF]
BMJ Glob HealthDlamini WJ, Melesse SF, Mwambi HG.
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NEO: NEuro-Inspired Optimization-A Fractional Time Series Approach. [PDF]
Front Physiol, 2021 Chatterjee S, Das S, Pequito S.
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Forecasting tuberculosis epidemics using an autoregressive fractionally integrated moving average model: a 17-year time series analysis. [PDF]
J Glob HealthWang Y +9 more
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The effect of COVID-19 on long memory in returns and volatility of cryptocurrency and stock markets. [PDF]
Chaos Solitons Fractals, 2021 Lahmiri S, Bekiros S.
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Time trends and persistence of the return difference between growth and value investment strategies. [PDF]
PLoS OneMonge M, Hurtado R, Infante J.
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