• Title/Summary/Keyword: 자기 회귀

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Forecasting attendance in the Korean professional baseball league using GARCH models (일반화 자기회귀 조건부 이분산 모형을 이용한 한국프로야구 관중수의 예측)

  • Lee, Jang-Taek;Bang, So-Young
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.6
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    • pp.1041-1049
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    • 2010
  • In Korean professional baseball, attendance is the largest source of revenue for development of professional baseball and the highest concern of professional baseball teams. So, if there is demand forecasting model, it will be helpful for pennant chasers to work out the strategies for drawing attendance. For this reason, this research intends to suggest the model which estimates Korean professional baseball's attendance and uses all usable variables which have an effect on attendance in limited circumstances. We supposed that dependent variable is attendance as well as several independent variables and error term are homoscedastic variance. And then, we compared the models which assume conditional heteroscedastic variance like GARCH and EGARCH with GARCH-t models which use the assumption that error term's distribution follows student-t distribution. In result of that, we could confirm that the models which were made by using GARCH(1,1)-t made estimates the most accurately among the several models considered.

Models for forecasting food poisoning occurrences (식중독 발생 예측모형)

  • Yeo, In-Kwon
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.6
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    • pp.1117-1125
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    • 2012
  • The occurrence of food poisoning is usually modeled by meteorological variables like the temperature and the humidity. In this paper, we investigate the relationship between food poisoning occurrence and climate variables in Korea and compare Poisson regression and autoregressive moving average model to select the forecast model. We confirm that lagged climate variables affect the food poisoning occurrences. However, it turns out that, from the viewpoint of the prediction, the number of previous occurrences is more influential to the current occurrence than meteorological variables and Poisson regression model is less reliable.

Efficient Estimation of Regression Coefficients in Regression Model with Moving Average Process (오차항이 이동평균과정을 따르는 회귀모형에서 회귀계수의 효율적 추정에 관한 연구)

  • 송석현;이종협;김기환
    • The Korean Journal of Applied Statistics
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    • v.12 no.1
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    • pp.109-124
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    • 1999
  • 일반적으로 오차항이 자기상관되어 있는 선형회귀 모형에서는 회귀계수에 대한 보통최소제곱추정량이 효율적이지 못 하다고 알려져 있다. 그러나 이러한 일반화선형회귀모형에서 독립변수의 형태에 따라서는 OLSE의 사용 가능성을 제시하는 모형이 있다. 본 연구에서는 오차항이 일차 이동평균 과정을 따르는 선형회귀모형에서 여러 추정량들 (GLSE, APX, MAPX)에 대한 OLSE의 상대효율함수를 유도하고 비교 분석하고자 한다. 특히 소표본에서 정확한 상대효율값을 구하여 OLSE의 효율성이 크게 떨어지지 않거나 효율성이 나은 회귀모형들을 제시한다.

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A Study on Speech Recognition using Recurrent Neural Networks (회귀신경망을 이용한 음성인식에 관한 연구)

  • 한학용;김주성;허강인
    • The Journal of the Acoustical Society of Korea
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    • v.18 no.3
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    • pp.62-67
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    • 1999
  • In this paper, we investigates a reliable model of the Predictive Recurrent Neural Network for the speech recognition. Predictive Neural Networks are modeled by syllable units. For the given input syllable, then a model which gives the minimum prediction error is taken as the recognition result. The Predictive Neural Network which has the structure of recurrent network was composed to give the dynamic feature of the speech pattern into the network. We have compared with the recognition ability of the Recurrent Network proposed by Elman and Jordan. ETRI's SAMDORI has been used for the speech DB. In order to find a reliable model of neural networks, the changes of two recognition rates were compared one another in conditions of: (1) changing prediction order and the number of hidden units: and (2) accumulating previous values with self-loop coefficient in its context. The result shows that the optimum prediction order, the number of hidden units, and self-loop coefficient have differently responded according to the structure of neural network used. However, in general, the Jordan's recurrent network shows relatively higher recognition rate than Elman's. The effects of recognition rate on the self-loop coefficient were variable according to the structures of neural network and their values.

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주가시계열에 대한 확률미분방정식(確率微分方程式)의 모수(母數) 추정(推定)과 자본시장의 운동법칙(運動法則)

  • Lee, Il-Gyun
    • The Korean Journal of Financial Management
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    • v.15 no.2
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    • pp.279-337
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    • 1998
  • 이 논문에서는 주가가 확률과정, 즉 확률미분방정식에 의하여 생성되는가를 검정하고 주가의 운동법칙을 규명한다. 일별종합주가지수가 양수의 완전시계열상관을 갖고 있으며, 더욱이 3년 정도의 시차까지 의미있는 시계열상관을 갖고 있음이 발견되었다. 수익률과 가격변화의 시계열상관도 존재하고 시계열은 정상성(定常性)을 갖고 있다. 마팅게일에 의하여 주가가 생성되고있지 않음이 밝혀졌다. 한국증권거래소에서 계산하고 있는 일별 종합주가지수를 포함한 41개 산업별 지수를 사용하여 자본시장의 운동법칙을 규명하기 위하여 가장 많이 이용하고 있는 세개의 확률미분방정식을 검정하였다. 각 주가지수들이 온스타인 울렌벡 브라운 운동과정과 평균회귀과정을 따르지 않고 있다는 것이 발견되었다. 그러나 주가가 편류를 갖는 일반 기하 브라운 운동과정에 의하여 생성되고 있음이 검정을 통하여 확인되었다. 평균회귀과정에 의하여 주가가 생성되지 않는다는 발견은 의외라 할 수 있다. 주가가 온스타인 울렌벡 과정을 따르지 않는다는 것은 주가가 제 1계 정상적 자기회귀과정이 아니라는 것을 의미한다. 일별종합주가지수는 제 4계 자기회귀과정에 의하여 생성된다. 가격변화와 수익률의 생성함수는 제 4계 자기회귀과정이다. 종합주가지수의 제 1계 시계열상관계수는 1이다. 상당히 큰 시차를 갖을 때까지 시계열상관이 대략적으로 1을 유지하고 있다. 따라서 지수가 마팅게일을 따르고 있지 않다. 이 점은 가격변화와 수익률에 있어서도 유사하다. 가격변화, 수익률, 대수수익률의 제 1계 시계열상관이 0.1로 유의적이다. 따라서 수익도 마팅게일 과정을 따르고 있지 않다. 증권가격은 세 번에 걸쳐 구조의 번화가 발생하였다. 구조의 변화가 발생할 때마다 평균가격이 상승하였다. 이와 같은 현상은 장기적 기대가격이 미지일 가능성이 배제되지 않는다. 단기적 기대 주가가 알려진 반면 장기적 기대 주가가 미지라면 평균회귀과정은 장기적 기대주가로 회귀하고 있는 과정이므로 장기기대 주가의 미지성이 평균회귀 과정의 기각을 유도하게 된다. 우리나라의 투자자들은 무위험자산과 위험을 동시에 고려하여 투자활동을 전개하고 있음이 발견되었다. 선형의 효용함수를 갖는 위험중립적 태도의 투자자가 아니다. 위험기피형 효용함수 아래에서 투자활동을 수행하고 있는 합리적 투자자들이라 할 수 있다. 뿐 만 아니라 자신의 평생에 걸친 소비를 소비가 이루어지는 각 기마다 가급적 일정하게 하는 소비행동을 목표로 삼고 소비와 투자에 대한 의사결정을 내리고 있음이 실증분석을 통하여 밝혀졌다. 투자자들은 무위험 자산과 위험성 자산을 동시에 고려하여 포트폴리오를 구성하는 투자활동을 행동에 옮기고 있다.

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The Longitudinal Relationship between Self-directed Learning Ability and Career Maturity using Autoregressive Cross-lagged Modeling by Middle and High School Students in Seoul (자기주도학습능력과 진로성숙도 간 자기회귀교차지연 효과검증: 서울지역 중·고등학생을 중심으로)

  • Jung, Joo-Young;Park, Kyun-Yeal;Lee, In-su;Lee, Su-jin
    • Journal of vocational education research
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    • v.35 no.4
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    • pp.89-107
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    • 2016
  • The purpose of this study was to verify the causal relationship between self-directed learning ability and career maturity by Middle and High School Students in Seoul. This study used Seoul Education Longitudinal Study(SELS) data. Using autoregressive cross-lagged modeling, the results was followed. first, self-directed learning ability value was had a statistically significant positive effect in accordance with the time course from middle school 1st grade to high school 3rd grade. Second, career maturity also had a statistically significant positive effect in accordance with the time course from middle school 1st grade to high school 3rd grade. Third, previous self-directed learning ability had significant positive effect on the later career maturity, but the previous career maturity had no significant effect on later self-directed learning ability.

A study on longitudinal relationship with academic stress, math self-efficacy, and math class engagement : Using auto regressive cross-lagged model (학업스트레스, 수학자기효능감, 수학수업참여에 관한 종단연구 : 자기회귀교차지연모형을 적용하여)

  • Song, Hyo seob;Jung, Hee sun
    • The Mathematical Education
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    • v.61 no.2
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    • pp.359-373
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    • 2022
  • This study aims to examine the differences in the longitudinal relationship between academic stress, mathematics self-efficacy, and engagement in mathematics class according to the math achievement level. According to the results, academic stress, math self-efficacy, and math class engagement were stable over time for the high and low groups. Also, In the high group, math self-efficacy had a negative longitudinal mediation effect in the influence of academic stress to math class engagement. Whereas, in the low group math class engagement had a positive longitudinal mediation effect in the influence of academic stress to math self-efficacy. This means that the academic stress affects differently according to the math achievement level, and mathematics teachers should reflect these results in their teaching/learning strategies so that students can increase their mathematics self-efficacy along with their engagement in mathematics classes.

Causal Analysis between the Korean and the U.S. Monthly Business Conditions (한미 월간 경기동향의 선행성 분석)

  • Kim, Tae-Ho
    • The Korean Journal of Applied Statistics
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    • v.22 no.1
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    • pp.17-28
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    • 2009
  • This study attempts to perform the statistical test for the causality between the Korean and the U.S. business conditions in association with the lead-lag relationship between the domestic stock price and the business condition. Their causal relationships are clearly identified after the outbreak of the IMF financial crisis. The vector autoregression for the corresponding period appears to reflect the strong interrelationships between the market variables and the dependency of the domestic business conditions on the U.S. market. The estimation results validate the leading effect of the stock price and the U.S. business behavior.

A Comparison of Robust Parameter Estimations for Autoregressive Models (자기회귀모형에서의 로버스트한 모수 추정방법들에 관한 연구)

  • Kang, Hee-Jeong;Kim, Soon-Young
    • Journal of the Korean Data and Information Science Society
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    • v.11 no.1
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    • pp.1-18
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    • 2000
  • In this paper, we study several parameter estimation methods used for autoregressive processes and compare them in view of forecasting. The least square estimation, least absolute deviation estimation, robust estimation are compared through Monte Carlo simulations.

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Comparison of Forecasting Performance in Multivariate Nonstationary Seasonal Time Series Models (다변량 비정상 계절형 시계열모형의 예측력 비교)

  • Seong, Byeong-Chan
    • Communications for Statistical Applications and Methods
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    • v.18 no.1
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    • pp.13-21
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    • 2011
  • This paper studies the analysis of multivariate nonstationary time series with seasonality. Three types of multivariate time series models are considered: seasonal cointegration model, nonseasonal cointegration model with seasonal dummies, and vector autoregressive model in seasonal differences that are compared for forecasting performances using Korean macro-economic time series data. The cointegration models produce smaller forecast errors in short horizons; however, when longer forecasting periods are considered the vector autoregressive model appears preferable.