Results 11 to 20 of about 367 (185)
Data & Policy, 2022 The aim of the present article is to evaluate the use of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model in predicting spatially and temporally localized political violent events using the Integrated Crisis Early Warning System ...
Tamir Libel
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Mathematics, 2021 The peaks-over-threshold (POT) method has a long tradition in modelling extremes in environmental variables. However, it has originally been introduced under the assumption of independently and identically distributed (iid) data. Since environmental data
Pushpa Dissanayake +3 more
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Kubik, 2015 Model ARFIMA (Autoregressive Fractionally Integrated Moving Average) merupakan pengembangan dari model ARIMA yang pertama kali dikenalkan oleh Granger dan Joyeux (1980). Sedangkan Hosking (1981) memperkenalkan sifat jangka panjang (long memory) pada data
Rini Cahyandari, Rima Erviana
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MODELLING FOR THE WAVELET COEFFICIENTS OF ARFIMA PROCESSES [PDF]
Journal of Time Series Analysis, 2014 AbstractWe consider a model for the discrete nonboundary wavelet coefficients of autoregressive fractionally integrated moving average (ARFIMA) processes in each scale. Because the utility of the wavelet transform for the long‐range dependent processes, which many authors have explained in semi‐parametrical literature, is approximating the transformed ...
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InPrime, 2019 Wind speed is one of the most important weather factors in the landing and takeoff process of airplane because it can affect the airplane's lift. Therefore, we need a model to predict the wind speed in an area.
Devi Ila Octaviyani +2 more
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Journal of Hydrology and Hydromechanics, 2014 Short term streamflow forecasting is important for operational control and risk management in hydrology. Despite a wide range of models available, the impact of long range dependence is often neglected when considering short term forecasting.
Szolgayová Elena +3 more
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Predicting BRICS stock returns using ARFIMA models [PDF]
Applied Financial Economics, 2014 This article examines the existence of long memory in daily stock market returns from Brazil, Russia, India, China and South Africa (BRICS) countries and also attempts to shed light on the efficacy of autoregressive fractionally integrated moving average (ARFIMA) models in predicting stock returns.
Aye, Goodness Chioma +5 more
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Forecasting energy futures volatility based on the unbiased extreme value volatility estimator
IIMB Management Review, 2017 This paper uses the opening, high, low, and closing prices of five energy futures to estimate and model volatility based on the unbiased extreme value volatility estimator (the Add RS estimator).
Dilip Kumar
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A Forecasting Model for Japan's Unemployment Rate [PDF]
Eurasian Journal of Business and Economics, 2010 This note aims to achieve a parsimonious fractionally-integrated autoregressive and moving average (ARFIMA) model for recent time series data of Japan's unemployment rate. A brief review of the ARFIMA model is provided, leading to econometric modeling of
Takamitsu KURITA
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Gaziantep Üniversitesi Sosyal Bilimler Dergisi, 2021 Bu çalışmada, Türkiye’nin 2010 – 2020 dönemine ait ülke Kredi Temerrüt Takası Primlerinin finansal zaman serisi olarak özellikleri araştırılmış, parametrik ve yarı parametrik ön testler uygulanmıştır.
Mustafa Çevik, Süleyman Serdar Karaca
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