Results 301 to 310 of about 412,561 (324)

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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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.
Dwayne A. Rollier, Chin-Sheng Alan Kang, David D. Bedworth   +2 more
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Autoregressive Integrated Moving Average (ARIMA) Models for Birth Forecasting [PDF]

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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THE EFFECT OF AGGREGATION ON PREDICTION IN AUTOREGRESSIVE INTEGRATED MOVING‐AVERAGE MODELS

Journal of Time Series Analysis, 1993
Abstract.Letxtbe a time series generated by an autoregressive integrated moving‐average process ARIMA(p, d, q). The non‐overlapping aggregate series also follows an ARIMA process. Thus, the prediction of the aggregated observations could be done by either the disaggregate model or the aggregate model.
J. Cardosc Neto, Luiz Koodi Hotta
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A Monte Carlo study of autoregressive integrated moving average processes

Journal of Econometrics, 1978
Abstract Six of the simpler ARMA type models are examined with respect to properties of a variety of proposed estimators of unknown parameters. The findings suggest that if only one estimation method were available to a researcher the choice should probably be maximum likelihood.
Warren T. Dent, An-Sik Min
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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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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 ...
Jeffrey Pai, Nalini Ravishanker
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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, SQ, Li, WK
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A Direct Basic Form for Predictors of Autoregressive Integrated Moving Average Processes

Biometrika, 1975
SUMMARY A direct method of formulating predictors of autoregressive integrated moving average processes is given which has certain advantages over the three basic forms proposed by Box & Jenkins (1970). Some ways of making use of this representation are discussed.
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Prediction of Raw Material Price Using Autoregressive Integrated Moving Average

2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2020
In a highly competitive manufacturing industry, it is necessary to reduce logistics cost for remaining competitiveness and increasing business profitability. One of several causes primarily influencing logistics cost is inventory to support fluctuation of raw material price and decision makers when and how much raw material is purchased.
Nutthaya Hankla, Ganda Boonsothonsatit
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