• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.981-996
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• 2003

Bayesian inference is considered for switching mean models with the ARMA errors. We use noninformative improper priors or uniform priors. The fractional Bayes factor of O'Hagan (1995) is used as the Bayesian tool for detecting the existence of a single change or multiple changes and the usual Bayes factor is used for identifying the orders of the ARMA error. Once the model is fully identified, the Gibbs sampler with the Metropolis-Hastings subchains is constructed to estimate parameters. Finally, we perform a simulation study to support theoretical results.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.4
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• pp.47-52
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• 2006

Using the equivalent ARMA model representation of the mixed random errors, we propose Klaman filter design methods for aided INS(Inertial Navigation System) which contains the gyroscope mixed random errors. At first step, considering the characteristic of indirect feedback Kalman filter used in the aided INS, we perform the time difference of equivalent ARMA model. Next, according to the order of the time differenced ARMA model, we achieve the state space conversion of that by two methods. If the order of AR part is greater than MA part, we use controllable or observable canonical form. Otherwise, we establish the state apace equation via the method that several step ahead predicts are included in the state variable, where we can derive high and low order models depending on the variable which is compensated from gyroscope output. At final step, we include the state space equation of gyroscope mixed random errors into aided INS Kalman filter model. Through the simulation, we show that both the high and low order filter models proposed give less navigation errors compared to the conventional filter which assume the mixed random errors as white noise.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1477-1488
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• 2013

In this study, we analyze the dependence structure of KOSPI and NYSE indices based on a two-step estimation procedure. In the rst step, we adopt ARMA-GARCH models with Gaussian mixture innovations for marginal processes. In the second step, time-varying copula parameters are estimated. By using these, we measure the dependence between the two returns with Kendall's tau and Spearman's rho. The two dependence measures for various copulas are illustrated.

• 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.

• Journal of applied mathematics & informatics
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• v.4 no.1
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• pp.135-135
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• 1997

Times series modeling plays an important role in the field of engineering, Statistics, Biomedicine etc. Model identification is one of crucial steps in the modeling of an AutoRegreesive Moving Average(ARMA(p, q)) process for real world problems. Many techniques have been developed in the literature (Salas et al., McLeod et al. etc.) for the identification of an ARMA(p, q) Model. In this paper, a new technique called The Generalised Parameters Technique is formulated for seasonal and non-seasonal ARMA model identification. This technique is very simple and can e applied to any given time series. Initial estimates of the AR parameters of the ARMA model are also obtained by this method. This model identification technique is validated through many theoretical and simulated examples.

• 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.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1257-1268
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• 2014

Since the introduction of KTX (Korea Tranin eXpress) in Korea reilway market, number of passengers using KTX has been greatly increased in the market. Thus, demand forecasting for KTX passengers has been played a importantant role in the train operation and management. In this paper, we study several time series models and compare the models based on considering special days and others. We used the MAPE (Mean Absolute Percentage Errors) to compare the performance between the models and we showed that the Reg-AR-GARCH model outperformanced other models in short-term period such as one month. In the longer periods, the Reg-ARMA model showed best forecasting accuracy compared with other models.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1373-1383
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• 2016

The wind energy industry and wind power generation have increased; consequently, the stable supply of the wind power has become an important issue. It is important to accurately predict the wind power with short-term basis in order to make a reliable planning for the power supply and demand of wind power. In this paper, we first analyzed the speed, power and the directions of the wind. The neural network and the time series models (ARMA, ARMAX, ARMA-GARCH, Holt Winters) for wind power generation forecasting were compared based on mean absolute error (MAE). For one to three hour-ahead forecast, ARMA-GARCH model was outperformed, and the neural network method showed a better performance in the six hour-ahead forecast.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.225-230
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• 1988

In this paper, a new method for determining the order of AR, ARMA processes based on PLS (predictive least square) principle is proposed, This method using modified lattice algorithm which has additional step is amenable to on-line or adaptive operation and is more accurate than any other mpthod. Some computer simulations are presented to show the efficiency of the proposed algorithms.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.85-93
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• 1995

Hannan and Rissanen proposed an innovation regression method of ARMA modeling, which is composed of three stages. Its second-stage is to choose orders of the ARMA model using the BIC, which needs a lot of calculation to estimate several regression models. We are going to present a simple and efficient algorithm for the second stage using a special property of triangular Toeplitz matrices.