• Water for future
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• v.18 no.1
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• pp.75-83
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• 1985

The applicability of ARMA(1, 1) multiseason model, which is in the beginning stage of active researches in the field of synthetic generation is evaluated with the streamflow data at the Nakdong stage gauging station on the main stem of the Nakdong River. The method of parameter estimation for the modelis reviewed and the statistical analysis of the generated seasonal streamflows such as corrlogram analysis and the computation of moments is made. The results obtained by ARMA(1, 1) multiseason model are compared with the historical streamflow data and also with those by two other multiseason models, namely, Thomas-Fiering model and Matalas AR(1) multiseason model. The seasonal streamflows grnerated by three multiseason models were annually summed up to form respective annual flow series whose statistics were compared with those of the annual flow series generated by three annual models, namely, AR(1), Matalas AR(1), and ARMA(1, 1) annual models. The possibility of ARMA(1, 1) multiseason model for the simultaneous generation of seasonal and annual streamflows is also evaluated.

• The Journal of the Acoustical Society of Korea
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• v.11 no.1
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• pp.67-79
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• 1992

For effective on-line ARMA parameter estimation, a covariance type ARMA fast transversal filter (FTF) algorithm is presented. The proposed algorithm is a covariance type implementation of ELS(Extended Least Squares) estimator and it is a fast time update recursion which is based on the fact that the correlation matrix of ARMA model satisfies the shift invariance property in each sub-block. The geometric approach is used in the derivation of the proposed algorithm. It takes small computational burden of 13N+37 MADPR(Multiplication And Division Per Recursion). Also, AR and MA orders can be independetly and arbitrarily specified.

• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.211-218
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• 2007

Nonlinear ARMA model $X_n\;=\;h(X_{n-1},{\cdots},X_{n-p},e_{n-1},{\cdots},e_{n-p})+e_n$ is considered and easy-to-check sufficient condition for strict stationarity of {$X_n$} without some irreducibility or continuity assumption is given. Threshold ARMA(p, q) and momentum threshold ARMA(p, q) models are examined as special cases.

• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.237-245
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• 2007

Nonlinear asymmetric time series models have the growing interest in econometrics and finance. Threshold model is one of the successful asymmetric model. We consider a smooth transition ARMA model which converges a.s. to a threshold ARMA model and show that the smooth transition ARMA model admits a stationary measure, provided a suitable condition on the coefficients of the autoregressive parts of the different regimes is satisfied. Stationarity of a smooth transition GARCH model is also obtained.

• Journal of Information Technology Application
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• v.1 no.3_4
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• pp.69-90
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• 1999

본 논문은 시계열자료의 ARMA 모형화를 위해 계층적(Hierarchical) 문제해결 방식인 인공신경망 기초 의상결정트리분류기상의 인공신경망 구조를 개선하여 지역문제(Local Problem)를 해결하는 복수개의 인공신경망 결과를 Dempster's rule of combination을 이용하여 종합하는 병행적인 (Parallel) ARMA 모형활르 위한 방법론을 제시함으로써 의사결정트리분류기에 근거한 방법론의 단점을 보완하였다. 본 논문에서 제시한 ARMA 모형화를 위한 방법론은 세 단계로 구성되어 있다: 1) ESACF 특성 벡터 추출단계; 2) 개별 인공신경망에 의한 부분적 모델링 단계; 3) Conflict Resolution 단계, 제시한 방법론을 검증하기 위해 모의실험용 자료와 실제 시계열자료를 이용하여 제시된 방법론을 검증하였으며 실험결과 기존 연구에 비해 ARMA 모형화와 정확도가 높은 것으로 나타났다.

• 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.

• Asia pacific journal of information systems
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• v.1 no.1
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• pp.63-86
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• 1991

In this paper, our concern is the artificial neural network-based patten classification, when can resolve the difficulties in the Autoregressive Moving Average(ARMA) model identification problem To effectively classify a time series into an approriate ARMA model, we adopt the Multi-layered Backpropagation Network (MLBPN) as a pattern classifier, and Extended Sample Autocorrelation Function (ESACF) as a feature extractor. To improve the classification power of MLBPN's we suggest an integrated neural network system which consists of an AR Network and many small-sized MA Networks. The output of AR Network which will gives the MA order. A step-by-step training strategy is also suggested so that the learned MLBPN's can effectively ESACF patterns contaminated by the high level of noises. The experiment with the artificially generated test data and real world data showed the promising results. Our approach, combined with a statistical parameter estimation method, will provide a way to the automation of ARMA modeling.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.462-462
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• 2015

이 논문에서는 시 공간적 토양수분 변화를 파악하기 위해 다년간 축적된 실측 토양수분 데이터를 이용하여 단변량 시계열 분석을 하였다. 지형에 따른 토양수분 변화를 알아보기 위해 경기도 파주에 위치한 설마천 유역의 산지사면 중 한 단면을 선정하였으며, 깊이에 따른 변동성은 깊이 10cm와 30cm에서 측정한 토양수분 데이터를 이용하여 분석하였다. 또한, 연도별 토양수분의 변화를 파악하고 토양수분을 예측하기 위해 2010-2013년의 토양수분 데이터를 일단위로 단변량 모델링을 시도하였다. 그 결과, 연도별 변화에 따른 경향성은 보이지 않았으며 대부분의 지점에서 ARMA(1, 1) 또는 ARMA(1, 0) 모형으로 모의되었다. 2시간 간격의 1-2개월 단기간 토양수분 데이터를 모의한 선행연구와 달리 본 연구에서는 낮은 차수의 모형을 보였다. 지형적 토양수분 거동을 살펴보면 상부사면에 위치하고 있는 지점에서는 모두 ARMA(1, 1)로 표현되지만 하부사면에 위치한 지점들은 연도나 심도에 따라 ARMA(1, 0)으로 모의된다. 단변량 모형의 정확도를 알아보기 위해 R2와 RMSE를 비교하였다. 10cm 깊이에서는 경향성을 보이지 않으나, 30cm 깊이에서는 사면하부로 갈수록 R2는 작아지고 RMSE는 커져, 하부사면에서의 모델링이 상부사면에 비해 정확도가 낮음을 보였다. 또한 2012년 토양수분 자료를 이용하여 2013년 토양수분을 예측하기 위해 2012년 매개변수와 2013년 전일 데이터를 이용하여 예측하고자 하는 일단위 토양수분을 구하였다. 그 결과 $R^2=0.646-0.807$, RMSE=1.758-4.802의 정확도를 나타냈다.

• Journal of Advanced Marine Engineering and Technology
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• v.41 no.1
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• pp.56-61
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• 2017

This paper presents a data prediction method and its application to haptic-based teleoperation systems. In general, time delays inevitably occur during data transmission in a network environment, which degrades the overall performance of haptic-based teleoperation systems. To address this situation, this paper proposes an autoregressive moving average (ARMA) model-based data prediction algorithm for estimating model parameters and predicting future data recursively in real time. The proposed method was applied to haptic data captured every 5 ms while bilateral haptic interaction was carried out by two users with an object in a virtual space. The results showed that the prediction performance of the proposed method had an error of less than 1 ms when predicting position-level data 100 ms ahead.

• Transactions of the Korean Society of Automotive Engineers
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• v.1 no.3
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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.