• Korean Management Science Review
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• v.1
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• pp.68-80
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• 1984

Box-Jenkins 시계열 분석법은 변수에 관한 정보가 부족하거나 너무 많은 변수가 영향을 미치고 있는 경우에도 과학적인 예측치를 구할 수 있는 단기예측 방법이다. Box-Jenkins 모형은 자동회귀 모형(Autoregressive Model), 이동평균 모형 (Moving average Model), 계절적 시계열 모형을 통합한 일반적인 모형이기 때문에 특별한 불안정성을 보이지 않는 경우에는 모두 모형화 할 수 있으며, 모형에 관계된 계수의 수를 최소화 하면서 만족스러운 모형을 찾을 수 있다. Box-Jenkins예측방법은 모형선정, 매개변수추정, 적합성 검정의 3단계를 반복으로 수행함으로써 최적모형에 이르게 하게 하고 있기 때문에 최소의 가능한 모형으로부터 시작하여 부적당한 부분을 제거시켜 나감으로써 시행착오의 과정을 최소화 할 수 있다. 일반 사용자가 Box-Jenkins 시계열 분석법을 쉽게 사용할 수 있도록 Box-Jenkins Package가 개발되었으며 여기서는 KAIST 전산 개발 센터에 설치된 Package를 소개하고 그 사용예를 보였다.

• Journal of Korean Society of Forest Science
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• v.95 no.6
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• pp.778-783
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• 2006

Price prediction is essential to decisions of investment and shipment in oak mushroom cultivation. But predicting the prices of oak mushroom is very difficult because there are so many uncertain factors affecting the demand and the supply in the market. The Box-Jenkins methodology is one of strong tools in price prediction especially for the short-term using historical observations of time series. In this paper, the Box-Jenkins methodology is applied to find a model to forecast future oak mushroom prices. And out-of-sample test was conducted to check out the prediction accuracy. The result shows the high accuracy except for market disturbance period affected by unexpected weather change and reveals the usefulness of the model.

• Journal of Preventive Medicine and Public Health
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• v.22 no.2 s.26
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• pp.189-196
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• 1989

In January 1988, district medical insurance plan was executed on a national scale in Korea. We conducted an evaluation of the impact of execution of district medical insurance plan on number of hospital patients: number of outpatients; and occupancy rate. This study was carried out by Box-Jenkins time series analysis. We tested the statistical significance with intervention component added to ARIMA model. Results of our time series analysis showed that district medical insurance plan had a significant effect on the number of outpatients and occupancy rate. Due to this plan the number of outpatients had increased by 925 patients every month which is equivalent to 8.3 percents of average monthly insurance outpatients in 1987, and occupancy rate had also increased by 0.12 which is equivalent to 16 percents of that in 1987.

• Journal of Korean Society on Water Environment
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• v.20 no.3
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• pp.223-230
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• 2004

The QUAL2E and Box-Jenkins time series model were applied to the Miho river, a main tributary of the Geum river, to predict water quality. The models are widely used to predict water quality in rivers and watersheds because of its accuracy. As results of the study, we concluded as follows: Pollutant loadings in upper stream of Miho river were determined to 57,811 kgBOD/d, 19,350 kgTN/d, and 5,013 kgTP/d. The loading of TN in Mushim river was 19,450 kgTN/d, respectively. As the mass loadings were compared with pollutant sources, it concluded that the farming livestock contributed highly to mass emissions of BOD and TP and the population contributed to TN mass loading. The observed water quality values were applied to the models to verify and the models were used to predict the water quality. The QUAL2E Model predicted the concentrations of DO, BOD, TN and TP with high accuracy, but not for E-Coli. The Box-Jenkins time series model also showed high prediction for DO, BOD and TN. However, the concentrations of TP and E-Coli were poorly predicted. The result shows that the QUAL2E model is more applicable in Miho basin for prediction of water quality compared to Box-Jenkins time series model.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.271-280
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• 2017

Real-estate values and related economics are often the first read newspaper category. We are concerned about the opinions of experts on the forecast for real estate prices. The Box-Jenkins ARIMA model is a commonly used statistical method to predict housing prices. In this article, we tried to predict housing prices by combining independent component analysis (ICA) in multivariate data analysis and the Box-Jenkins ARIMA model. The two independent components for both the selling price index and the long-term rental price index were extracted and used to predict the future values of both indices. In conclusion, it has been shown that the actual indices and the forecast indices using ICA are more comparable to the forecasts of the ARIMA model alone.

• The Pure and Applied Mathematics
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• v.11 no.4
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• pp.275-285
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• 2004

In this paper, the time series models for the number of reported death claims of compulsory automobile liability insurance in Korea are studied. We found that IMA${(0, 1, 1)}\;{\times}\;{(0, 1, 1)}_{12}$ would the most appropriate model for the number of reported claims by the Box-Jenkins method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.16 no.28
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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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.

• Water for future
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• v.17 no.4
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• pp.281-291
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• 1984

In our Koreans river basins there are many of monthly rainfall data, but unfortrnately streamflow data needed are rare. Analysing monthly rainfall data of Somjin river basin, the stochastic theory model for calculation of monthly streamflow series of that region is determined. The model is composed of Box & Jenkins stansfer function plus ARIMA residual models. This linear stochastic differenced time series equation models can adapt themselves to the structure and variety of rainfall, streamflow data on the assumption of the stationary covarience. The fiexibility of Box-Jenkins method consists mainly in the iterative technique of building an AIRMA model from observations and by the use of autocorrelation functions. The best models for Somjin river basin belong to the general calss: $Y_t=($\omega$o-$\omega$_1B) C_iX_t+$\varepsilon$t$ $Y_t$ monthly streamflow, $X_t$ : monthly rainfall, $C_i$ :monthly run-off, $$\omega$o-$\omega$_1$ : transfer parameter, $$\varepsilon$_t$ : residual The streamflow series resulted from the proposed model is satisfactory comparing with the exsting streamflow data of Somjin gauging station site.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.7
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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.