• Communications for Statistical Applications and Methods
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• 제2권2호
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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.

• Journal of the Korean Statistical Society
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• 제31권3호
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• pp.301-314
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• 2002

For autoregressive moving average (ARMA) models, robust unit root tests are developed using M-estimators. The tests are parametric in the sense ARMA parameters are estimated jointly with unit roots. A Monte-Carlo experiment reveals superiority of the parametric tests over the semipararmetric tests of Lucas (1995a) in terms of both empirical sizes and powers.

• 한국음향학회:학술대회논문집
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• 한국음향학회 2000년도 하계학술발표대회 논문집 제19권 1호
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• pp.203-206
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• 2000

입체음향 시스템에서 모노음에 방향감을 제어하기 위한 방법으로 FIR 필터 형태의 머리전달함수( HRTF : Head-Related Transfer Function)를 사용한다. 그러나 이때 사용되는 FIR형태의 머리전달함수는 높은 차수를 가지고 있어 실시간 음상정위 처리가 어려운 문제점을 가지고 있다. 본 논문에서는 FIR 형태의 머리전달함수를 ARMA 시스템 인지기법을 이용하여 저차의 IIR필터 형태로 모델링하여 실시간 데이터 처리가 가능하도록 하였다. 본 논문에서 제안하는 ARMA 시스템 인지기법을 이용하게 되면 주어진 고차의 FIR형태의 머리전달함수를 다양한 안정성을 갖는 IIR모델들을 얻을 수 있으며, 이들 중 적절한 스펙트럼오차를 갖는 저차의 IIR모델을 선택 할 수 있다.

• Journal of Electrical Engineering and Technology
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• 제11권6호
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• pp.1548-1555
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• 2016

This paper presents an application of time series analysis in hourly wind speed simulation and forecast in Jeju Island, Korea. Autoregressive - moving average (ARMA) model, which is well in description of random data characteristics, is used to analyze historical wind speed data (from year of 2010 to 2012). The ARMA model requires stationary variables of data is satisfied by power law transformation and standardization. In this study, the autocorrelation analysis, Bayesian information criterion and general least squares algorithm is implemented to identify and estimate parameters of wind speed model. The ARMA (2,1) models, fitted to the wind speed data, simulate reference year and forecast hourly wind speed in Jeju Island.

• Communications for Statistical Applications and Methods
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• 제10권3호
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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.

• 응용통계연구
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• 제17권3호
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• pp.403-418
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• 2004

시간에 따라 얻어진 공간 자료를 공간시계열 자료라 하며 이러한 자료를 분석하기 위해 사용되는 모형이 공간시계열 모형이다. 최근 곤충학과 생태학에서 공간시계열 모형을 이용한 연구가 활발히 진행되고 있다. 본 논문에서는 온실에 있는 곤충의 마리수를 ARMA 모형과 자기회귀 오차모형을 이용한 공간시계열 모형으로 분석하였다. 자료에 포함된 이상점은 분산도(Variogram) 추정에 많은 영향을 주기 때문에 Mugglestone (2000)의 이상점 수정법을 이용하여 수정하였다. 공간시계열 모형들과 시계열 요인을 배제한 공간모형을 MSE와 MAPE를 이용하여 비교하였다.

• Journal of the Korean Data and Information Science Society
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• 제24권6호
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• pp.1551-1559
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• 2013

In this paper, we analyze the Korean Won/Japanese 100 Yen exchange rate data based on the ARMA-GARCH model, and perform the test for detecting the parameter changes. As a test statistics, we employ the cumulative sum (CUSUM) test for ARMA-GARCH model, which is introduced by Lee and Song (2008). Our empirical analysis indicates that the KRW/JPY exchange rate series experienced several parameter changes during the period from January 2000 to December 2012, which leads to a fitting of AR-IGARCH model to the whole series.

• 한국자동차공학회논문집
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• 제1권3호
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• pp.55-62
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• 1993

Autoregressive moving average(ARMA) model which is a time domain parametric modeling method is implemented for modeling and reproducing characteristics of exhaust noise of an automobile in various RPM range. Experiments have been carried out using 9 set of exhaust noise signals measured at 1,000-3,000 RPM range. Characteristics of sampled signals were estimated using ARMA modeling and Akaike's FPE(final prediction error) criterion to define exact model structure and for model validation. The digital filter consisted of the esitmated ARMA(70,1) model parameters was programed to reproduce exhaust noise. The spectral analysis of reproduced noise is very close to original. The results show that our approaching technique for reproducing acoustic characteristics is valid and feasible to apply in the field of noise quality control.

• 말소리와 음성과학
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• 제2권4호
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• pp.145-151
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• 2010

In this paper, a robust feature compensation method is proposed for improving the performance of speech recognition. The proposed method is incorporated into the auto-regressive moving average (ARMA) based feature compensation. We employ variable weights for the ARMA filter according to the degree of speech activity, and pass the normalized cepstral sequence through the weighted ARMA filter. Additionally when normalizing the cepstral sequences in training, the cepstral means and variances are estimated from total training utterances. Experimental results show the proposed method significantly improves the speech recognition performance in the noisy and reverberant environments.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권7호
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• pp.423-427
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• 2005

In this paper, we proposed a new ARMA model identification which estimate the parameters to make the current prediction error uncorrelated with the past one. As good properties of the proposed method, we show the uniqueness, consistency of the estimate and asymptotic normality of the estimation error. Via simulation results, we show that the proposed method give good estimates for various systems which have different power spectrum. Moreover, the estimation of gyroscope random errors shows that the proposed method is applicable to the real data.