Results 41 to 50 of about 367 (185)
Jurnal LebesgueThe Autoregressive Fractionally Integrated Moving Average (ARFIMA) model is a development of the ARIMA model with the differencing values being fractional numbers.
Muhammad Reja Sinaga +2 more
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S&P 500 microstructure noise components: empirical inferences from futures and ETF prices
Journal of Time Series Analysis, Volume 46, Issue 6, Page 1032-1063, November 2025.By studying the differences between futures prices and exchange‐traded fund prices for the S&P 500 index, original results are obtained about the distribution and persistence of the microstructure noise component created by positive bid‐ask spreads and discrete price scales.
Stephen J. Taylor
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
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Local powers of least‐squares‐based test for panel fractional Ornstein–Uhlenbeck process
Journal of Time Series Analysis, Volume 46, Issue 5, Page 997-1023, September 2025.In recent years, significant advancements have been made in the field of identifying financial asset price bubbles, particularly through the development of time‐series unit‐root tests featuring fractionally integrated errors and panel unit‐root tests.
Katsuto Tanaka, Weilin Xiao, Jun Yu
wiley +1 more source
Jurnal LebesgueOil price movements are highly volatile and tend to be influenced over extended periods, often displaying long memory effect. This study utilizes the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model, a long memory model, to analyze ...
Eza Syafri Ramadhani +2 more
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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
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
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
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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
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