• 응용통계연구
• /
• 제26권2호
• /
• pp.237-252
• /
• 2013

보험료 및 보험료 구성요소에 대한 예측모형은 합리적인 보험료 결정에 필수적이다. 본 연구에서는 가변수 회귀모형, 독립변수 추가모형, 자기회귀 오차모형, 계절형 ARIMA 모형, 개입모형 등 적정한 자동차 대물 손해보험료 추정에 사용되는 다양한 모형을 소개하였다. 또한 실제 자동차 대물 보험료 자료를 이용하여 각 모형을 이용하여 보험료, 심도, 빈도 등을 추정하였으며, 모형의 추정결과는 추정치와 실제 자료값의 차이에 근거한 RMSE(Root Mean Squared Errors) 값을 통해 비교하였다. 실제 자료 분석 결과, 자기회귀 오차모형이 가장 좋은 성능을 보여주는 것을 알 수 있었다.

• 한국대기환경학회:학술대회논문집
• /
• 한국대기환경학회 2008년도 춘계학술대회 논문집
• /
• pp.455-456
• /
• 2008

• 한국대기환경학회:학술대회논문집
• /
• 한국대기환경학회 2008년도 추계학술대회 논문집
• /
• pp.292-293
• /
• 2008

• 전기학회논문지
• /
• 제63권11호
• /
• pp.1497-1502
• /
• 2014

Electric load forecasting is essential for stable electric power supply, efficient operation and management of power systems, and safe operation of power generation systems. The results are utilized in generator preventive maintenance planning and the systemization of power reserve management. Development and improvement of electric load forecasting model is necessary for power system maintenance and operation. This paper proposes daily maximum electric load forecasting methods for the next 4 weeks with a seasonal autoregressive integrated moving average model and an exponential smoothing model. According to the results of forecasting of daily maximum electric load forecasting for the next 4 weeks of March, April, November 2010~2012 using the constructed forecasting models, the seasonal autoregressive integrated moving average model showed an average error rate of 6,66%, 5.26%, 3.61% respectively and the exponential smoothing model showed an average error rate of 3.82%, 4.07%, 3.59% respectively.

• Journal of the Korean Statistical Society
• /
• 제27권2호
• /
• pp.189-195
• /
• 1998

In this paper, we are concerned with the problem of testing the randomness of the coefficients in a first order autoregressive model. A consistent test based on prediction error is suggested. It is shown that under the null hypothesis, the test statistic is asymptotically normal.

• 응용통계연구
• /
• 제26권6호
• /
• pp.923-932
• /
• 2013

이 논문에서는 우리나라에서 발생하는 원인균별 식중독 발생건수를 예측하는 방법을 제안한다. 우리나라에서 보고되는 주별 식중독 발생 건수를 원인균로 나누면 자료에 많은 0의 관측값이 포함되어 있으며 식중독 발생 간에 종속성을 가진다. 이 현상을 모형화하기 위해 이 논문에서는 전체 식중독 건수를 자기회귀모형으로 예측하고 원인균별 식중독 발생 확률을 다범주 로짓모형으로 추정한다. 예측된 식중독 건수와 추정된 원인균별 식중독 발생 확률을 곱하여 원인균별 식중독 발생건수를 예측한다. 제안된 방법의 타당성을 확인하기 위해 평균제곱오차와 평균절대편차를 이용하여 제안 방법과 영과잉모형을 비교해 본다.

• 응용통계연구
• /
• 제29권1호
• /
• pp.257-266
• /
• 2016

비선형 모형인 확률계수 자기회귀 모형의 모수를 추정하기 위해 전체 데이터를 부표본으로 나누어 확률계수 ${\phi}(t)$가 초기값, ${\phi}(0)$를 갖는 특별한 경우를 제안하고 추정하였다. 모의 실험으로 부표본으로 나누어 확률계수 자기회귀 모형을 추정하는 더 바람직함을 확인하였다. 실증분석에서는 한국 Mumps 자료를 선형 모형인 자기회귀 모형과 확률 계수 자기회귀 모형에 각각 적합시켜 모수를 추정하고, PRESS 값을 비교하여 확률계수 자기회귀 모형의 예측이 더 우수함을 보였다.

• 대한전기학회논문지
• /
• 제45권4호
• /
• pp.511-516
• /
• 1996

In this paper, weekly maximum power demand forecasting method in consideration of temperature estimation using a time series model was presented. The method removing weekly, seasonal variations on the load and irregularities variation due to unknown factor was presented. The forecasting model that represent the relations between load and temperature which get a numeral expected temperature based on the past 30 years(1961~1990) temperature was constructed. Effect of holiday was removed by using a weekday change ratio, and irregularities variation was removed by using an autoregressive model. The results of load forecasting show the ability of the method in forecasting with good accuracy without suffering from the effect of seasons and holidays. Percentage error load forecasting of all seasons except summer was obtained below 2 percentage. (author). refs., figs., tabs.

• Journal of the Korean Data and Information Science Society
• /
• 제27권2호
• /
• pp.559-564
• /
• 2016

The Durbin-Watson (DW) test in regression model and the Ljung-Box (LB) test in ARMA (autoregressive moving average) model are typical examples of correlated error tests. The DW test is used for detecting autocorrelation of errors using the residuals from a regression analysis. The LB test is used for specifying the correct ARMA model using the first some sample autocorrelations based on the residuals of a tted ARMA model. In this article, simulations with four data generating processes have been carried out to evaluate their performances as correlated error tests. Our simulations show that the DW test is severely dependent on the assumed AR(1) model but isn't sensitive enough to reject the misspecified model and that the LB test reports lackluster performance in general.

• 한국대기환경학회지
• /
• 제24권5호
• /
• pp.604-612
• /
• 2008

The ozone concentration is one of the important environmental issue for measurement of the atmospheric condition of the country. This study focuses on applying the Autoregressive Error (ARE) model for analyzing the ozone data at middle part of the Gyeonggi-Do, Anyang monitoring site in Korea. In the ARE model, eight meteorological variables and four pollution variables are used as the explanatory variables. The eight meteorological variables are daily maximum temperature, wind speed, amount of cloud, global radiation, relative humidity, rainfall, dew point temperature, and water vapor pressure. The four air pollution variables are sulfur dioxide $(SO_2)$, nitrogen dioxide $(NO_2)$, carbon monoxide (CO), and particulate matter 10 (PM10). The result shows that ARE models both overall and monthly data are suited for describing the oBone concentration. In the ARE model for overall ozone data, ozone concentration can be explained about 71% to by the PM10, global radiation and wind speed. Also the four types of ARE models for high level of ozone data (over 80 ppb) have been analyzed. In the best ARE model for high level of ozone data, ozone can be explained about 96% by the PM10, daliy maximum temperature, and cloud amount.