Results 161 to 170 of about 5,691 (212)

A new automatic forecasting method based on explainable deep dendritic artificial neural network. [PDF]

Sci Rep
Bas E, Egrioglu E.
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Hybrid signal decomposition and deep learning framework for vehicle-vehicle crash forecasting. [PDF]

Sci Rep
Zhou J, Khattak A, Mao X, Chen F, Xing L, Zhang Z.   +5 more
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Infant mortality rates in Ghana: progress toward sustainable development goal 3. [PDF]

BMC Public Health
Opoku S, Adama ZK, Abuga S.
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Bayesian Inference for ARFIMA Models

Journal of Time Series Analysis, 2019
This article develops practical methods for Bayesian inference in the autoregressive fractionally integrated moving average (ARFIMA) model using the exact likelihood function, any proper prior distribution, and time series that may have thousands of observations.
Durham, Garland, Geweke, John, Porter-Hudak, Susan, Sowell, Fallaw   +3 more
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BAYESIAN ANALYSIS OF VECTOR ARFIMA PROCESSES

Australian Journal of Statistics, 1997
SummaryA general framework is presented for Bayesian inference of multivariate time series exhibiting long‐range dependence. The series are modelled using a vector autoregressive fractionally integrated moving‐average (VARFIMA) process, which can capture both short‐term correlation structure and long‐range dependence characteristics of the individual ...
Ravishanker, Nalini, Ray, Bonnie K.
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Consistent order selection for ARFIMA processes

The Annals of Statistics, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Huang, Hsueh-Han, Chan, Ngai Hang, Chen, Kun, Ing, Ching-Kang   +3 more
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Jeffrey's divergence between ARFIMA processes

Digital Signal Processing, 2018
Abstract The symmetric Kullback–Leibler divergence known as Jeffrey's divergence (JD) has found applications in signal and image processing, from radar clutter modeling to texture analysis. Recently, several studies were done on the JD between ergodic wide-sense stationary autoregressive (AR) and/or moving average (MA) processes.
Mahdi Saleh, Eric Grivel, Samir-Mohamad Omar   +2 more
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Adaptive ARFIMA models with applications to inflation

Economic Modelling, 2012
Abstract Many previous analyses of inflation have used either long memory or nonlinear time series models. This paper suggests a simple adaptive modification of the basic ARFIMA model, which uses a flexible Fourier form to allow for a time varying intercept.
Morana, C, Baillie, RT
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Regularised Estimators for ARFIMA Processes

IFAC Proceedings Volumes, 2012
Abstract Stochastic processes with long-range dependence are found in many applications. ARFIMA models can be used to characterise both their short-term correlations and the phenomenon of long-range dependence. Maximum likelihood estimates of the model parameters have nice statistical properties but are ill-conditioned and hard to compute.
Oskar Vivero, William P. Heath
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An Evaluation of ARFIMA Programs

Volume 9: 13th ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications, 2017
Strong coupling between values at different time that exhibit properties of long range dependence, non-stationary, spiky signals cannot be processed by the conventional time series analysis. The ARFIMA model, which employs the fractional order signal processing techniques, is the generalization of the conventional integer order models — ARIMA and ARMA ...
Kai Liu, Xi Zhang, YangQuan Chen
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