• Title/Summary/Keyword: ARMA

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An Algorithm for Hannan and Rissanen's ARMA Modeling Method

  • Chul Eung Kim;Byoung Seon Choi
    • 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.

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Unit Root Tests for Autoregressive Moving Average Processes Based on M-estimators

  • Shin, Dong-Wan;Lee, Oesook
    • Journal of the Korean Statistical Society
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    • v.31 no.3
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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.

Low-Order Modeling of HRTF using ARMA-System (ARMA시스템을 이용한 머리전달함수 저차 모델링)

  • Kim Hong-Choul;Lee Ou-Seb;Lee Won-Cheol
    • Proceedings of the Acoustical Society of Korea Conference
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    • spring
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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.

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Hourly Average Wind Speed Simulation and Forecast Based on ARMA Model in Jeju Island, Korea

  • Do, Duy-Phuong N.;Lee, Yeonchan;Choi, Jaeseok
    • Journal of Electrical Engineering and Technology
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    • v.11 no.6
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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.

Bayesian Inference for Switching Mean Models with ARMA Errors

  • Son, Young Sook;Kim, Seong W.;Cho, Sinsup
    • 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.

Space Time Data Analysis for Greenhouse Whitefly (온실가루이의 공간시계열 분석)

  • 박진모;신기일
    • The Korean Journal of Applied Statistics
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    • v.17 no.3
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    • pp.403-418
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    • 2004
  • Recently space-time model in spatial data analysis is widly used. In this paper we applied this model to analysis of greenhouse whitefly. For handling time component, we used ARMA model and autoregressive error model and for outliers, we adapted Mugglestone's method. We compared space-time models and geostatistic model with MSE and MAPE.

Testing the exchange rate data for the parameter change based on ARMA-GARCH model

  • Song, Junmo;Ko, Bangwon
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.6
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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.

A Study on the Reproduction of Acoustic Characteristics of a Car's Exhaust Noise Using Digital Filtering Technique (디지탈 필터링 기법(技法)을 이용(利用)한 자동차(自動車) 배기소음(排氣騷音)의 음향특성(音響特性) 재현(再現)에 관(關)한 연구(硏究))

  • Cho, J.H.;Lee, J.M.;Hwang, Y.
    • 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.

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Robust Speech Recognition Using Weighted Auto-Regressive Moving Average Filter (가중 ARMA 필터를 이용한 강인한 음성인식)

  • Ban, Sung-Min;Kim, Hyung-Soon
    • Phonetics and Speech Sciences
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    • v.2 no.4
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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.

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An ARMA Model Identification Method By Direct Whitening Of Prediction Error and Its Application to Estimation of Gyroscope Random Error (예측오차 직접 백색화에 의한 ARMA 모델 식별 기법 및 자이로 불규칙오차 추정에의 적용)

  • Seong, Sang-Man;Lee, Dal-Ho
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.54 no.7
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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.