Results 31 to 40 of about 4,943 (215)
Media Statistika, 2021 Currently the emergence of the novel coronavirus (Sars-Cov-2), which causes the COVID-19 pandemic and has become a serious health problem because of the high risk causes of death.
Puspita Kartikasari +2 more
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Annales Mathematicae Silesianae, 2018 In this survey paper we present a systematic methodology of how to identify origins of fractional dynamics. We consider three models leading to it, namely fractional Brownian motion (FBM), fractional Lévy stable motion (FLSM) and autoregressive ...
Weron Aleksander
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Adjusted Empirical Likelihood for Long-memory Time Series Models
, 2016 Empirical likelihood method has been applied to short-memory time series models by Monti (1997) through the Whittle's estimation method. Yau (2012) extended this idea to long-memory time series models. Asymptotic distributions of the empirical likelihood
Gamage, Ramadha D. Piyadi +2 more
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Comparing the bias and misspecification in ARFIMA models [PDF]
Journal of Time Series Analysis, 1997 We investigate the bias in both the short‐term and long‐term parameters for a range of autoregressive fractional integrated moving‐average (ARFIMA) models using both semi‐parametric and maximum likelihood (ML) estimation methods. The results suggest that, provided the correct model is estimated, the ML method outperforms the semi‐parametric methods in ...
Smith, Jeremy +2 more
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Environmetrics, Volume 37, Issue 3, April 2026.ABSTRACT We propose a new time series model for continuous data supported on the open unit interval (0,1)$$ \left(0,1\right) $$, motivated by applications in environmental and energy systems. The Matsuoka autoregressive moving average (MARMA) model combines the Matsuoka distribution‐a uniparametric member of the canonical exponential family‐as the ...
Guilherme Pumi +3 more
wiley +1 more source
On the invertibility in periodic ARFIMA models
, 2020 The present paper, characterizes the invertibility and causality conditions of a periodic ARFIMA (PARFIMA) models. We first, discuss the conditions in the multivariate case, by considering the corresponding p-variate stationary ARFIMA models. Second, we construct the conditions using the univariate case and we deduce a new infinite autoregressive ...
Amimour, Amine, Belaide, Karima
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Wavelet based long memory model for modelling wheat price in India
The Indian Journal of Agricultural Sciences, 2021 Agricultural time-series data concerning production, prices, export and import of several agricultural commodities is published by Indian government along with other private agricultural sectors every year.
RANJIT KUMAR PAUL +2 more
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A Fuzzy Framework for Realized Volatility Prediction: Empirical Evidence From Equity Markets
Journal of Forecasting, Volume 45, Issue 3, Page 1261-1291, April 2026.ABSTRACT This study introduces a realized volatility fuzzy time series (RV‐FTS) model that applies a fuzzy c‐means clustering algorithm to estimate time‐varying c latent volatility states and their corresponding membership degrees. These memberships are used to construct a fuzzified volatility estimate as a weighted average of cluster centroids.
Shafqat Iqbal, Štefan Lyócsa
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Nonfractional Memory: Filtering, Antipersistence, and Forecasting
, 2018 The fractional difference operator remains to be the most popular mechanism to generate long memory due to the existence of efficient algorithms for their simulation and forecasting.
Vera-Valdés, J. Eduardo
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Error and Model Misspecification in ARFIMA Process
Brazilian Review of Econometrics, 2001 In developing the long and short memory estimation, it is usually assumed that the innovations in the ARFIMA model are normally distributed. However, circumstances may occur where this assumption is not true. This paper uses Monte Carlo simulation to evaluate the robustness of different estimators of the fractional parameter in stationary and ...
Valderio A. Reisen +2 more
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