Results 301 to 310 of about 73,625 (339)

Bayesian analysis of autoregressive fractionally integrated moving‐average processes

Journal of Time Series Analysis, 1998
For the autoregressive fractionally integrated moving‐average (ARFIMA) processes which characterize both long‐memory and short‐memory behavior in time series, we formulate Bayesian inference using Markov chain Monte Carlo methods. We derive a form for the joint posterior distribution of the parameters that is computationally feasible for repetitive ...
Pai, Jeffrey S., Ravishanker, Nalini
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A GENERALIZED FRACTIONALLY INTEGRATED AUTOREGRESSIVE MOVING‐AVERAGE PROCESS

Journal of Time Series Analysis, 1996
Abstract. This paper considers the long memory Gegenbauer autoregressive movingaverage (GARMA) process that generalizes the fractionally integrated ARMA (ARFIMA) process to allow for hyperbolic and sinusoidal decay in autocorrelations. We propose the conditional sum of squares method for estimation (which is asymptotically equivalent to the maximum ...
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Testing for a Moving Average Unit Root in Autoregressive Integrated Moving Average Models

Journal of the American Statistical Association, 1993
Abstract Test procedures for detecting overdifferencing or a moving average unit root in Gaussian autoregressive integrated moving average (ARIMA) models are proposed. The tests can be used when an autoregressive unit root is a serious alternative but the hypothesis of primary interest implies stationarity of the observed time series. This is the case,
Pentti Saikkonen, Ritva Luukkonen
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Autoregressive integrated moving average in clinical trials

Clinical Investigation, 2013
when multiple repeated observations are available, it would make a lot of sense to measure the outcome in the form of a time series; accounting trends, seasonal effects ...
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Automatic Identification of Autoregressive Integrated Moving Average Time Series

A I I E Transactions, 1982
Abstract This paper discusses the development of a computer-oriented technique for automatically identifying nonseasonal Box-Jenkins ARIMA (p, d, q) models or multiplicative seasonal Box-Jenkins ARIMA (p, d, q)∗ (P, D, Q)s models for discrete univariate time series.
Chin-Sheng Alan Kang, David D. Bedworth, Dwayne A. Rollier   +2 more
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On Fractionally Integrated Autoregressive Moving-Average Time Series Models With Conditional Heteroscedasticity

Journal of the American Statistical Association, 1997
Abstract This article considers fractionally integrated autoregressive moving-average time series models with conditional heteroscedasticity, which combines the popular generalized autoregressive conditional heteroscedastic (GARCH) and the fractional (ARMA) models.
Ling, S, Li, WK
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Time Series Modeling and Forecasting Using Autoregressive Integrated Moving Average and Seasonal Autoregressive Integrated Moving Average Models

Instrumentation Mesure Métrologie, 2023
Vignesh Arumugam, Vijayalakshmi Natarajan   +1 more
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Autoregressive Integrated Moving Average (ARIMA) Models for Birth Forecasting

Journal of the American Statistical Association, 1977
Abstract Autoregressive integrated moving average (ARIMA) models are developed for birth time series, and their relationship with classical models for population growth is investigated. Parsimonious versions for the ARIMA models are obtained which retain the most important pieces of information including the length of generation of the population.
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GOLD PRICE PREDICTION USING AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA)

2020
Gold was first used as a standard means of exchange in 643 B.C when it was used to create coins. During this ear, wealth was then defined as a function of the amount of gold possessed by individuals or countries. The impact of gold on the economy of any nation has a direct correlation with the safety and security of most related investments in the ...
IGBOELİ, Uchenna, BABATUNDE, Abdulrauph Olanrewaju   +1 more
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Extreme Learning Machine-Autoregression Integrated Moving Average Composite Model

Advances in Science and Technology
Excessive carbon dioxide emissions are the primary factor causing global warming. Currently, models for controlling carbon dioxide emissions mainly focus on population, economy, and technology. A significant amount of research has been conducted on multivariate linear regression analysis encompassing factors such as population, GDP, and energy ...
Zi Wen Yin, Chun Jiang
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