Results 51 to 60 of about 467 (180)
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
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
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
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
Error and Model Misspecification in ARFIMA Process
Brazilian Review of Econometrics, 2001 In developing the long and short memory estimation, it is usually assumed that the innovations in the ARFIMA model are normally distributed. However, circumstances may occur where this assumption is not true. This paper uses Monte Carlo simulation to evaluate the robustness of different estimators of the fractional parameter in stationary and ...
Valderio A. Reisen +2 more
openaire +2 more sources
Fractional stochastic volatility model
Journal of Time Series Analysis, Volume 46, Issue 2, Page 378-397, March 2025.This article introduces a discrete‐time fractional stochastic volatility model (FSV) based on fractional Gaussian noise. The new model includes the standard stochastic volatility model as a special case and has the same limit as the fractional integrated stochastic volatility (FISV) model, which is the continuous‐time fractional Ornstein–Uhlenbeck ...
Shuping Shi, Xiaobin Liu, Jun Yu
wiley +1 more source
International Journal of Mathematics and Mathematical Sciences, Volume 2025, Issue 1, 2025.Accurate temperature forecasting is of paramount importance across various sectors, influencing decision‐making processes and impacting numerous aspects of daily life. This study analyzes temperature time series data from the Nairobi County in Kenya, aiming to develop accurate hybrid time series forecasting models.
John Kamwele Mutinda +3 more
wiley +1 more source
PM10 AIR QUALITY INDEX MODELING USING ARFIMA-GARCH METHOD: BUNDARAN HI AREA OF DKI JAKARTA PROVINCE
BarekengAir quality is an essential factor in urban life, and its’ assessment often relies on the concentration of measurable air pollution parameters. One critical parameter is Particulate Matter (PM), particularly PM10, which comprises solid or liquid ...
Susilo Hariyanto +2 more
doaj +1 more source
Malaysian Journal of Computing, 2020 The main purpose of this study is to compare the performances of univariate and bivariate models on four-time series variables of the crude palm oil industry in Peninsular Malaysia.
Pauline Jin Wee Mah, Nur Nadhirah Nanyan
doaj +1 more source
Estimation of parameters in ARFIMA processes
, 2020 It is known that, in the presence of short memory components, the estimation of the fractional parameter d in an Autoregressive Fractionally Integrated Moving Average, ARFIMA(p, d, q), process leads to some difficulties (Smith et al. (1997)). In this paper, we continue the efforts made by Smith et al. (1997) by conducting a simulation study to evaluate
Lopes, Silvia Regina Costa +2 more
openaire +1 more source