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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
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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
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Frontiers in Applied Mathematics and Statistics, 2022 IntroductionThe price of crude oil as an essential commodity in the world economy shows a pattern and identifies the component factors that influence it in the short and long term.
Dodi Devianto +4 more
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Evaluating the Effectiveness of GARCH Models in the Estimation of Systematic Risk in listed companies of the Tehran Stock Exchange [PDF]
Journal of Asset Management and Financing, 2020 The stock market of each country, in addition to reflecting its economic structure, is considered as an important source of capital Circulation of that country.
nemat rastgoo, Hossein panahian
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PEMODELAN DATA HARGA CABAI DENGAN PENDEKATAN DERET WAKTU FRAKSIONAL ARFIMA
Jurnal Lebesgue, 2023 Long-memory is a type of time series data that has a high correlation between long observation times. This can be seen from the autocorrelation function where the lag falls slowly over a long period. Such long-memory data can be modeled in the form of an
Elsa Wahyuni +2 more
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Journal of Computing Research and Innovation, 2021 Gold is known as the most valuable commodity in the world because it is a universal currency recognized by every single bank across the globe. Thus, many people were interested in investing gold since gold market was always steadier compared to other ...
Atiqa Nur Azza Mahmad Azan +2 more
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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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Combining long memory and level shifts in modeling and forecasting the volatility of asset returns [PDF]
, 2017 We propose a parametric state space model of asset return volatility with an accompanying estimation and forecasting framework that allows for ARFIMA dynamics, random level shifts and measurement errors.
Perron, Pierre, Varneskov, Rasmus T.
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Long-Range Dependence in Financial Markets: a Moving Average Cluster Entropy Approach [PDF]
, 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 ...
Carbone, Anna +2 more
core +3 more sources