• 물과 미래
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• 제17권4호
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• pp.281-291
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• 1984

한국하천유역의 강우량관측자료는 풍부하나 하천유량측정자료가 많고 섬진강 유역내의 압록과 송정의 유량관측기록이 비교적장기간에 것이 있고, 유속측정을 많이 하고 있으므로 본유역자료를 가지고 월유출량계열의 모형식을 유도하였다. 본모형식은 월강우량기록으로서 월유출량 산출식을 Box & Jenkins의 대체함수모형식에다 ARIMA의 잔차모형식을 가하여 유도한 것이다. 또 기 강우량과 유출량 자료간에는 잔차시계열이 정상공분산을 갖는다는 가정하에 모형식을 작성하였다. 자기상관 함수의 특성으로부터 ARIMA모형을 유도함에도 먼저 계산식으로 각변수를 산출하고, 이 변수를 다소조정반복시켜 가장 정확한 융통성있는 Box & Jenkins 방식의 모형식을 작성하였다. 섬진강에서 가장 적정모형식을 다음과 같은 일반식으로 주어졌다. 여기서 $Y_t=($\omega$o-$\omega$_1B) C_iX_t+$\varepsilon$t$ $Y_t$ 월유출량, $X_t$: 월 강우량, $C_i$: 월유출률, $$\omega$o-$\omega$_1$ : 대체변수 $$\varepsilon$_t$ : 잔차(임의오차성분) 섬진강수위관측소의 기 월유출량 기록자료로서 월유출량게열의 만족할만한 모형을 비교검토 연구작성하였다.

• 산업경영시스템학회지
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• 제20권44호
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• pp.243-255
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• 1997

Forecasting the future values of an observed time series is an important problem in many areas, including economics, traffic engineering, production planning, sales forecasting, and stock control. The purpose of this paper is aimed to discover the more efficient forecasting model through the parameter estimation and residual analysis among the quantitative method such as Winters' exponential smoothing model, Box-Jenkins' model, and Kalman filtering model. The mean of the time series is assumed to be a linear combination of known functions. For a parameter estimation and residual analysis, Winters', Box-Jenkins' model use Statgrap and Timeslab software, and Kalman filtering utilizes Fortran language. Therefore, this paper can be used in real fields to obtain the most effective forecasting model.

• 동아시아경상학회지
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• 제1권1호
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• pp.17-21
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• 2013

Purpose: Forecasting of petroleum consumption is useful in planning and management of petroleum production and control of air pollution. Research Design, Data and Methodology: ARMA models, sometimes called Box-Jenkins models after the iterative Box-Jenkins methodology usually used to estimate them, are typically applied to auto correlated time series data. Results: Petroleum consumption modeling plays a role key in big urban air pollution planning and management. In this study three models as, MLFF, MLFF with GARCH (1,1) and ARMA(1,1), have been investigated to model the petroleum consumption forecasts. Certain standard statistical parameters were used to evaluate the performance of the models developed in this study. Based upon the results obtained in this study and the consequent comparative analysis, it has been found that the MLFF with GARCH (1,1) have better forecasting results.. Conclusions: Survey of data reveals that deposit of government policies in recent yeas, petroleum consumption rises in Tehran and unfortunately more petroleum use causes to air pollution and bad environmental problems.

• 대한전자공학회논문지
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• 제17권3호
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• pp.1-11
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• 1980

전자공학분야와 관련분야(일반력학, 물리 및 수학등) 사이의 용어의 차이를 해소하기 위한 노력을 계속하였고 통계학의 석학Box 씨와 Jenkins씨의 time series analysis의 입문을 위한 주변설명과 용어소개를 꾀하였다. 끝으로 Wiener의 filter와 Kalman-Bucy의 Algorithm을 설명하고 Hadamard를 위시한 변환기술의 유리점을 정리하여 보았다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 추계 학술대회 학술발표 논문집
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• pp.76-79
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• 2003

퍼지 로직 시스템(FLS)은 다양한 분야에서 성공적으로 사용되고 있다 퍼지 로직 시스템의 멤버십 함수와 규칙은 언어적인 정보나 수치적 데이터를 사용하여 표현된다. 또한 이러한 정보나 데이터에는 불확실성과 노이즈 등이 존재한다. 그러나 단순한 퍼지 로직 시스템으로노이즈가 포함된 불확실한 정보를 효과적으로 다루고 표현하는 데는 한계가 있다. 그러므로 노이즈가 포함된 정보를 효율적으로 처리하기 위해 본 논문에서는 type-2 FLS를 이용한다. 노이즈가 포함되어 불확실한 정도를 정확한 값으로 표현하기 어려울 때, type-2 FLS은 보다 정확하게 정보들을 다를 수 있음을 보인다. 비선형 시계열 시스템인 Box-Jenkins 데이터를 이용하여 singleton Type-1 FLS과 non-singleton type-1 FLS의 결과 값을 확인하고 이의 성능을 type-2 FLS과 비교, 분석한다.

• 산업경영시스템학회지
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• 제22권51호
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• pp.1-10
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• 1999

In recent industry society, it is revealed that, as an increase in the use of automated manufacturing and process inspection technology, the data from mass production system exhibits some degrees of autocorrelation. The operation characteristics of traditional control charts developed under the independence assumption are adversely affected by the presence of serial correlation. Therefore, when autocorrelated construction contacted with time-series models explain, the time-series models are the Box-Jenkins forecast models which have been proposed as the best forecasting tool which allows for partitioning of variation into result from the autocorrelation structure and variation due to unusual but assignable causes. In this paper, for the AR(1) process of Box-Jenkins forecast models, when the constant term ξ are zero and different from zero, I want to analyze the sensitivity of (equation omitted), CUSUM and EWMA control chart for forecast residuals.

• 산업경영시스템학회지
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• 제16권28호
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• pp.31-37
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• 1993

Box-Jenkins models have some important limitations to the procedure : (a) They require a great deal of time, efforts and expertise for the model identification. (b) They require an extensive amount of past observations to identify an acceptable model. (c) The model selected is a constant model in time. Therefore, the Kalman Filter is recommended as a technique to overcome the three problems mentioned above. The research reported here uses the Kalman Filter algorithm to propose Kalman-AR(p) model. The data analysis shows that the Kalman-AR(p) model proposed can be used to resolve the problems of Box-Jenkins AR(p)model. It is seen that the Kalman Filter has great potentials for real-time industrial applications.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권7호
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• pp.462-467
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• 2005

In this paper, we analyzed normal gait acceleration signal by time series analysis methods. Accelerations were measured during walking using a biaxial accelerometer. Acceleration data were acquired from normal subjects(23 men and one woman) walking on a level corridor of 20m in length with three different walking speeds. Acceleration signals were measured at a sampling frequency of 60Hz from a biaxial accelerometer mounted between L3 and L4 intervertebral area. Each step signal was analyzed using Box-Jenkins method. Most of the differenced normal step signals were modeled to AR(3) and the model didn't show difference for model's orders and coefficients with walking speed. But, tile model showed difference with acceleration signal direction - vertical and lateral. The above results suggested the proposed model could be applied to unit analysis.

• Industrial Engineering and Management Systems
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• 제7권1호
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• pp.9-22
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• 2008

As an extension of a previous work by the authors (Song and Esogbue, 2006), a new algorithm for automated modeling of nonstationary seasonal time series is presented in this paper. Issues relative to the methodology for building automatically seasonal time series models and periodic time series models are addressed. This is achieved by inspecting the trend, estimating the seasonality, determining the orders of the model, and estimating the parameters. As in our previous work, the major instruments used in the model identification process are correlograms of the modeling errors while the least square method is used for parameter estimation. We provide numerical illustrations of the performance of the new algorithms with respect to building both seasonal time series and periodic time series models. Additionally, we consider forecasting and exercise the models on some sample time series problems found in the literature as well as real life problems drawn from the retail industry. In each instance, the models are built automatically avoiding the necessity of any human intervention.

• Advances in environmental research
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• 제4권4호
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• pp.233-246
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• 2015

High amounts of air pollution in crowded urban areas are always considered as one of the major environmental challenges especially in developing countries. Despite the errors in air pollution prediction, the forecasting of future data helps air quality management make decisions promptly and properly. We studied the air quality of the Aqdasiyeh location in Tehran using factor analysis and the Box-Jenkins time series methods. The Air Quality Control Company (AQCC) of the Municipality of Tehran monitors seven daily air quality parameters, including carbon monoxide (CO), Nitrogen Monoxide (NO), Nitrogen dioxide ($NO_2$), $NO_x$, ozone ($O_3$), particulate matter ($PM_{10}$) and sulfur dioxide ($SO_2$). We applied the AQCC data for our study. According to the results of the factor analysis, the air quality parameters were divided into two factors. The first factor included CO, $NO_2$, NO, $NO_x$, and $O_3$, and the second was $SO_2$ and $PM_{10}$. Subsequently, the Box- Jenkins time series was applied to the two mentioned factors. The results of the statistical testing and comparison of the factor data with the predicted data indicated Auto Regressive Integrated Moving Average (0, 0, 1) was appropriate for the first factor, and ARIMA (1, 0, 1) was proper for the second one. The coefficient of determination between the factor data and the predicted data for both models were 0.98 and 0.983 which may indicate the accuracy of the models. The application of these methods could be beneficial for the reduction of developing numbers of mathematical modeling.