Results 51 to 60 of about 4,901 (213)
Combining long memory and level shifts in modeling and forecasting the volatility of asset returns [PDF]
, 2015 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.
core
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
A brief history of long memory: Hurst, Mandelbrot and the road to ARFIMA [PDF]
, 2016 Long memory plays an important role in many fields by determining the behaviour and predictability of systems; for instance, climate, hydrology, finance, networks and DNA sequencing.
Franzke, Christian +3 more
core +2 more sources
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
FORECASTING FRESH WATER AND MARINE FISH PRODUCTION IN MALAYSIA USING ARIMA AND ARFIMA MODELS
Malaysian Journal of Computing, 2018 Malaysia is surrounded by sea, rivers and lakes which provide natural sources of fish for human consumption. Hence, fish is one source of protein supply to the country and fishery is a sub-sector that contribute to the national gross domestic product ...
P.J.W. Mah, N.N.M. Zali, N.A.M. Ihwal, N.Z. Azizan
doaj +1 more source
Вестник Самарского университета: Естественнонаучная серия, 2023 For modeling in time series, models with fractional differences are widely used. The best known model is the ARFIMA (autoregressive fractionally integrated moving average) model.
Dmitriy V. Ivanov
doaj +1 more source
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
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
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
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