Modelling event-related oscillations by autoregressive integrated moving average
, 2004 Öz bulunamadı.
Erzengin, OU, Sumbuloglu, V, Karakas, S
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A fractionally integrated autoregressive moving average approach to forecasting tourism demand
Tourism Management, 2008 The primary aim of this paper is to incorporate fractionally integrated ARMA (p, d, q) (ARFIMA) models into tourism forecasting, and to compare the accuracy of forecasts with those obtained by previous studies. The models are estimated using the volume of monthly international tourist arrivals in Singapore.
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Kick Risk Forecasting and Evaluating During Drilling Based on Autoregressive Integrated Moving Average Model [PDF]
, 2019 Hu Yin +4 more
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Generalized and Subset Integrated Autoregressive Moving Average Bilinear Time Series Models [PDF]
, 2012 Generalized integrated autoregressive moving average bilinear model which is capable of achieving stationary for all non linear series is proposed and compared with subset generalized integrated autoregressive moving average bilinear model using the ...
J. F., Ojo
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Machine Learning Approaches to Forecast the Realized Volatility of Crude Oil Prices
Journal of Forecasting, EarlyView.ABSTRACT This paper presents an evaluation of the accuracy of machine learning (ML) techniques in forecasting the realized volatility of West Texas Intermediate (WTI) crude oil prices. We compare several ML algorithms, including regularization, regression trees, random forests, and neural networks, to several heterogeneous autoregressive (HAR) models ...
Talha Omer +3 more
wiley +1 more source
Prediction of women and Children's hospital outpatient numbers based on the autoregressive integrated moving average model. [PDF]
Heliyon, 2023 Lin Y, Wan C, Li S, Xie S, Gan Y, Lu Y.
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A Deep Learning Framework for Forecasting Medium‐Term Covariance in Multiasset Portfolios
Journal of Forecasting, EarlyView.ABSTRACT Forecasting the covariance matrix of asset returns is central to portfolio construction, risk management, and asset pricing. However, most existing models struggle at medium‐term horizons, several weeks to months, where shifting market regimes and slower dynamics prevail.
Pedro Reis, Ana Paula Serra, João Gama
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REGCMPNT : A Fortran Program for Regression Models with ARIMA Component Errors [PDF]
RegComponent models are time series models with linear regression mean functions and error terms that follow ARIMA (autoregressive-integrated-moving average) component time series models.
William R. Bell
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