Results 61 to 70 of about 23,794,745 (244)
Forecasting Digital Asset Return: An Application of Machine Learning Model
International Journal of Finance &Economics, Volume 30, Issue 3, Page 3169-3186, July 2025.ABSTRACT In this study, we aim to identify the machine learning model that can overcome the limitations of traditional statistical modelling techniques in forecasting Bitcoin prices. Also, we outline the necessary conditions that make the model suitable.
Vito Ciciretti +4 more
wiley +1 more source
An Empirical Investigation of the Usefulness of ARFIMA Models for Predicting Macroeconomic and Financial Time Series [PDF]
, 2004 This paper addresses the notion that many fractional I(d) processes may fall into the ?empty box? category, as discussed in Granger (1999). We present ex ante forecasting evidence based on an updated version of the absolute returns series examined by ...
Bhardwaj, Geetesh, Swanson, Norman R.
core
Gold price modeling in Indonesia using ARFIMA method
Journal of Physics: Conference Series, 2019 Abstract Gold investment is the best choice to control finance. Gold is easy to resell if there is a financial need at the unpredictable moment. The data of gold price in Indonesia is a long-term memory data series or a time series data that has a long-term dependency.
D Safitri +3 more
openaire +1 more source
Local Whittle estimation in time‐varying long memory series
Journal of Time Series Analysis, Volume 46, Issue 4, Page 647-673, July 2025.The memory parameter is usually assumed to be constant in traditional long memory time series. We relax this restriction by considering the memory a time‐varying function that depends on a finite number of parameters. A time‐varying Local Whittle estimator of these parameters, and hence of the memory function, is proposed.
Josu Arteche, Luis F. Martins
wiley +1 more source
Network traffic prediction based on ARFIMA model
, 2013 ARFIMA is a time series forecasting model, which is an improved ARMA model, the ARFIMA model proposed in this article is demonstrated and deduced in detail. combined with network traffic of CERNET backbone and the ARFIMA model,the result shows that,compare to the ARMA model, the prediction efficiency and accuracy has increased significantly, and not ...
Zhou, Dingding +2 more
openaire +2 more sources
Improved Trend Analysis With EOFs and Application to Warming of Polar Regions
International Journal of Climatology, Volume 45, Issue 7, 15 June 2025.Introducing a variation of EOF analysis, we obtain an insignificant Antarctic trend between 1979 and 2023 of (0.13 ± 0.17) K/decade. The first principal component completely captures the trend for land regions of the order of the size of most countries.
Ewan T. Phillips, Holger Kantz
wiley +1 more source
THE IMPACT OF THE FINANCIAL CRISIS ON LONG MEMORY: EVIDENCE FROM EUROPEAN BANKING INDICES [PDF]
Annals of the University of Oradea: Economic Science, 2014 We have analyzed the impact of the financial crisis on the existence of the long term dependency for European banking indices. By estimating Hurst Exponent, ARFIMA and FIGARCH models we found that major financial crisis such as, Mexican, Asian and ...
Pece Andreea Maria +3 more
doaj
Forecasting West Texas Intermediate Crude Oil Price: Stochastic Differential Approach [PDF]
مدلسازی اقتصادسنجی, 2018 Uncertainty in oil markets has led economic researchers to the use of stochastic processes. The purpose of this paper, is the use of stochastic differential models to predict the crude oil price of West Texas Intermediate (WTI) and compare the ...
ramin khochiani, younes nademi
doaj +1 more source
Identifying influential individuals and predicting future demand of chronic kidney disease patients
Decision Sciences, Volume 56, Issue 2, Page 123-143, April 2025.ABSTRACT To ensure high service quality, managers need to personalize treatment options and meet their customer demands. Our research is motivated by the need to better anticipate and prepare for that. We develop a generalizable framework that is the first to address two healthcare risk management goals: (1) identifying high risk and stable‐demand ...
Zlatana D. Nenova, Valerie L. Bartelt
wiley +1 more source
Stock market volatility simulation with the LSTM neural network
Вестник Пермского университета: Серия ЭкономикаIntroduction. Stock market volatility simulation and forecast are relevant issues which could contribute into lower risks and higher revenues of the market transactions.
Dmitry Aleksandrovich Patlasov +1 more
doaj +1 more source