Hybridization of long short-term memory neural network in fractional time series modeling of inflation [PDF]
Frontiers in Big Data, 2023 Inflation is capable of significantly impacting monetary policy, thereby emphasizing the need for accurate forecasts to guide decisions aimed at stabilizing inflation rates.
Erman Arif +4 more
doaj +2 more sources
Considerations for Applying Entropy Methods to Temporally Correlated Stochastic Datasets [PDF]
Entropy, 2023 The goal of this paper is to highlight considerations and provide recommendations for analytical issues that arise when applying entropy methods, specifically Sample Entropy (SampEn), to temporally correlated stochastic datasets, which are representative
Joshua Liddy, Michael Busa
doaj +2 more sources
Long-Range Dependence in Financial Markets: A Moving Average Cluster Entropy Approach. [PDF]
Entropy (Basel), 2020 A perspective is taken on the intangible complexity of economic and social systems by investigating the underlying dynamical processes that produce, store and transmit information in financial time series in terms of the \textit{moving average cluster ...
Murialdo P, Ponta L, Carbone A.
europepmc +5 more sources
Forecasting realised volatility using ARFIMA and HAR models [PDF]
Quantitative Finance, 2019 Recent literature provides mixed empirical evidence with respect to the forecasting performance of ARFIMA and HAR models. This paper compares the forecasting performance of both models using high frequency data of 100 stocks representing 10 business sectors for the period 2000-2010.
Marwan Izzeldin +2 more
exaly +3 more sources
Cumhuriyet Science JournalThe research delved into analysing the stochastic characteristics of Nigeria's Real GDP, the exchange rate of the Naira to US Dollar, and the inflation rate employing Autoregressive fractionally integrated moving average (ARFIMA) and the Autoregressive ...
Ayoade Adewole
doaj +3 more sources
IDENTIFICACIÓN DE MODELOS ARFIMA
Revista de la Facultad de Ciencias, 2016 Desde la introducción de los modelos fraccionalmente integrados ARFIMA para series de tiempo con memoria larga, ha surgido un gran interés en el estudio de sus propiedades y áreas de aplicación.
Elkin Castaño-Vëlez
doaj +4 more sources
Evaluating the long-term impact of COVID-19-associated public health interventions on zoonotic and vector-borne diseases in China: an interrupted time series analysis [PDF]
Journal of Translational MedicineBackground The long-term impact of COVID-19-associated public health interventions on zoonotic and vector-borne infectious diseases (ZVBs) remains uncertain.
Yongbin Wang +8 more
doaj +2 more sources
Fractional and fractal processes applied to cryptocurrencies price series [PDF]
Journal of Advanced Research, 2021 Introduction: Cryptocurrencies have been attracting the attention from media, investors, regulators and academia during the last years. In spite of some scepticism in the financial area, cryptocurrencies are a relevant subject of academic research ...
S.A. David +3 more
doaj +2 more sources
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.
Goodness C Aye +2 more
exaly +3 more sources
ARFIMA Model for Short Term Forecasting of New Death Cases COVID-19 [PDF]
E3S Web of Conferences, 2020 COVID-19 is an infectious disease that can spread from one person to another and has a high potential for death. The infection of COVID-19 is spreading massive and fast that causes the extreme fluctuating data spread and long memory effects.
Kartikasari Puspita +2 more
doaj +1 more source