• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.585-592
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• 2013

As the needs for alternative energy and renewable energy increase, there has been a lot of investment in developing wind energy, which does not cause air pollution nor the greenhouse gas effect. Wind energy is an environment friendly energy that is unlimited in its resources and is possible to be produced wherever the wind blows. However, since wind energy heavily relies on wind that has unreliable characteristics, it may be difficult to have efficient energy transmissions. For this reason, an important factor in wind energy forecasting is the estimation of available wind power. In this study, Gunsan wind farm data was used to compare ARMA model to neural network model to analyze for more accurate prediction of wind power generation. As a result, the neural network model was better than the ARMA model in the accuracy of the wind power predictions.

• Industrial Engineering and Management Systems
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• v.8 no.4
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• pp.247-256
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• 2009

In mass production industries such as steel making that have large equipment, sudden stops of production process due to machine failure can cause severe problems. To prevent such situations, machine diagnosis techniques play important roles. Many methods have been developed focusing on this subject. In this paper, we propose a method for the early detection of the failure on rotating machine, which is the most common theme in the machine failure detection field. A simplified method of calculating autocorrelation function is introduced and is utilized for ARMA model identification. Furthermore, an absolute deterioration factor such as Bicoherence is introduced. Machine diagnosis can be executed by this simplified calculation method of system parameter distance with weight. Proposed method proved to be a practical index for machine diagnosis by numerical examples.

• Industrial Engineering and Management Systems
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• v.1 no.1
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• pp.79-86
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• 2002

This paper proposes a new algorithm for estimating ARMA model parameters. In estimating ARMA model parameters, several methods such as generalized least square method, instrumental variable method have been developed. Among these methods, the utilization of a bootstrap type algorithm is known as one of the effective approach for the estimation, but there are cases that it does not converge. Hence, in this paper, making use of a cross correlation function and utilizing the relation of structural a priori knowledge, a new bootstrap algorithm is developed. By introducing theoretical relations, it became possible to remove terms, which is liable to include much noise. Therefore, this leads to robust parameter estimation. It is shown by numerical examples that using this algorithm, all simulation cases converge while only half cases succeeded with the previous one. As for the calculation time, judging from the fact that we got converged solutions, our proposed method is said to be superior as a whole.

• Journal of the Korean Statistical Society
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• v.36 no.2
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• pp.183-200
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• 2007

We consider a nonlinear autoregressive moving average model with nonlinear GARCH errors, and find sufficient conditions for the existence of a strictly stationary solution of three related time series equations. We also consider a geometric ergodicity and functional central limit theorem for a nonlinear autoregressive model with nonlinear ARCH errors. The given model includes broad classes of nonlinear models. New results are obtained, and known results are shown to emerge as special cases.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.267-269
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• 2000

Using the fuzzified coefficients, ARMA processes can be extended to fuzzy time series model. In this paper, the estimation of parameters in the fuzzy time series model with asymmetric triangular fuzzy coefficients is studied. Nonlinear programming is applied to get solutions of parameters.

• Journal of the Korean Statistical Society
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• v.21 no.1
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• pp.47-58
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• 1992

The autocorrelation function structures of bilinear time series model BL(p, q, r, s), $r \geq s$ are obtained and shown to be analogous to those of ARMA(p, l), l=max(q, s). Simulation studies are performed to investigate the adequacy of Akaike information criteria for identification between ARMA(p, l) and BL(p, q, r, s) models and for determination of orders of BL(p, q, r, s) models. It is suggested that the model of having minimum Akaike information criteria is selected for a suitable model.

• Communications of the Korean Mathematical Society
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• v.21 no.3
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• pp.533-541
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• 2006

We consider a autoregressive moving average process of order p and q with Markov switching coefficients and find sufficient conditions for irreducibility of the process. Identifying small sets is also examined.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.511-527
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• 2015

For time series analysis, power transformation (especially log-transformation) is widely used for variance stabilization or normalization for stationary ARMA(p, q) model. A simple and naive back transformed forecast is obtained by taking the inverse function of expectation. However, this back transformed forecast has a bias. Under the assumption that the log-transformed data is normally distributed. The unbiased back transformed forecast can be obtained by the expectation of log-normal distribution; consequently, the property of this back transformation was studied by Granger and Newbold (1976). We investigate the sensitivity of back transformed forecasts under several different underlying distributions using simulation studies.

• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.723-730
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• 2009

Missing values in time series can be treated as unknown parameters and estimated by maximum likelihood or as random variables and predicted by the expectation of the unknown values given the data. The purpose of this study is to impute missing values which are regarded as the maximum likelihood estimator and random variable in incomplete data and to compare with two methods using ARMA model. For illustration, the Mumps data reported from the national capital region monthly over the years 2001 ${\sim}$ 2006 are used, and results from two methods are compared with using SSF(Sum of square for forecasting error).

• Journal of the Korean Statistical Society
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• v.33 no.2
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• pp.169-189
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• 2004

Under the assumption of default priors, such as noninformative priors, Bayesian model determination and parameter estimation of regression models with stationary and invertible ARMA errors are developed under exact full likelihoods. The default Bayes factors, the fractional Bayes factor (FBF) of O'Hagan (1995) and the arithmetic intrinsic Bayes factors (AIBF) of Berger and Pericchi (1996a), are used as tools for the selection of the Bayesian model. Bayesian estimates are obtained by running the Metropolis-Hastings subchain in the Gibbs sampler. Finally, the results of numerical studies, designed to check the performance of the theoretical results discussed here, are presented.