• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.809-815
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• 2007

Since Bollerslev(1986), the GARCH model has been popular in analysing the volatility of the financial time series. In real data analysis, practitioners conventionally put the normal assumption on the innovation random variables of the GARCH model, which is often violated. In this paper, we analyse the domestic financial data based on the GARCH(1,1) model and among existing normality tests, perform the Jarque-Bera test based on the residuals. It is shown that the innovation based on the GARCH(1,1) model dose not follow the normality assumption.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.3
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• pp.264-270
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• 2011

A new method for creating a logical data model is proposed. The logical data model developed by the method defines table, primary key, foreign key, and fields. The framework of the logical data model is constructed by modeling the relationships between events and their related entity types. The proposed method consists of a series of objective and quantitative decisions such as maximum cardinality of relationships and functional dependency between the primary key and attributes. Even beginners to database design can use the methology as long as they understand such basic concepts about relational databases as primary key, foreign key, relationship cardinality, parent-child relationship, and functional dependency. The simple and systematic approach minimizes decision errors made by a database designer. In practial database design the method creates a logical data model in Boyce-Codd normal form unless the user of the method makes a critical decision error, which is very unlikely.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.441-450
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• 2010

In this paper we consider the problem of forecasting binomial time series, modelled by the binomial autoregressive model. This paper considers proposed by McKenzie (1985) and is extended to a higher order by $Wei{\ss}$(2009). Since the binomial autoregressive model is a Markov chain, we can apply the earlier work of Bu and McCabe (2008) for integer valued autoregressive(INAR) model to the binomial autoregressive model. We will discuss how to compute the h-step-ahead forecast of the conditional probabilities of $X_{T+h}$ when T periods are used in fitting. Then we obtain the maximum likelihood estimator of binomial autoregressive model and use it to derive the maximum likelihood estimator of the h-step-ahead forecast of the conditional probabilities of $X_{T+h}$. The methodology is illustrated by applying it to a data set previously analyzed by $Wei{\ss}$(2009).

• Journal of Korean Society of Water and Wastewater
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• v.10 no.2
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• pp.88-103
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• 1996

The objective of this study is to develop stochastic and deterministic models which could be used to synthesize water application time series. Adaptive models using mulitivariate ARIMA(Transfer Function Model) are developed for daily urban water use forecasting. The model considers several variables on which water demands is dependent. The dynamic response of water demands to several factors(e.g. weekday, average temperature, minimum temperature, maximum temperature, humidity, cloudiness, rainfall) are characterized in the model by transfer functions. Daily water use data of Kumi city in 1992 are employed for model parameter estimation. Meteorological data of Seonsan station are utilized to input variables because Kumi has no records about the meteorological factor data.To determine the main factors influencing water use, autocorrelogram and cross correlogram analysis are performed. Through the identification, parameter estimation, and diagnostic checking of tentative model, final transfer function models by each month are established. The simulation output by transfer function models are compared to a historical data and shows the good agreement.

• Asia pacific journal of information systems
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• v.11 no.4
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• pp.99-111
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• 2001

The prediction of stock price index is a very difficult problem because of the complexity of stock market data. It has been studied by a number of researchers since they strongly affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network(BPN). Finally, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.

• Journal of Korean Society of Transportation
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• v.13 no.1
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• pp.63-82
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• 1995

This paper deals with the forecasting model for traffic accident. Its objective is to develop the appropriate model to project the accident of Chung-Chong Region. Two types of models between motorization (M) and personal hazard (P) are tested : One is inverted-U (bell type) curve and the other is increasing (or decreasing) curve. The statiscal and sensitivity analyses show that exponential model (type III) and multiplicative model (type II) are well fit to the given cross-sectional and time-series accident data. The model projects that the fatality per 100, 000 persons of Chung-Chong region, when the motorization level (M) is 0.2, would be in the range between 18 and 77 persons. The paper concludes that the accident level is the function of motorization and the result of implementing the safety policy of a region.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.4238-4245
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• 2023

In order to predict the lifetime of InP Heterojunction Bipolar Transistor (HBT) devices and related circuits in the space radiation environment, a novel model including gamma radiation effects is proposed in this paper. Based on the analysis of radiation-induced device degradation effects including both DC and AC characteristics, a set of empirical expressions describing the device degradation trend are presented and incorporated into the Keysight model. To validate the effective of the proposed model, a series of radiation experiments are performed. The correctness of the novel model is validated by comparing experimental and simulated results before and after radiation.

• Archives of Metallurgy and Materials
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• v.64 no.3
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• pp.819-822
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• 2019

Uniaxial tensile tests were performed on porcine skin to investigate the tensile stress-strain constitutive characteristic at quasistatic deformations using uniaxial tensile tests. Experimental results were then used to determine the parameters of the various constitutive model types for rubber, including the Mooney-Rivlin, Yeoh, Ogden, and others. The Prony series viscoelastic model was also calibrated based on the stress relaxation test. To investigate the calibrated constitutive equations (visco-hyperelastic), the falling impact test was conducted. From the viewpoint of the maximum impact load, the error was approximately 15.87%. Overall, the Ogden model predicted the experimental measurements most reasonably. The calibrated constitutive model is expected to be of practical use in describing the mechanical properties of porcine skin.

• Journal of Korean Society of Transportation
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• v.34 no.1
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• pp.95-106
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• 2016

Air transportation demand is consistently increasing in Korea due to economic growth and low cost carriers. For this reason, airport expansion plans are being discussed in Korea. Therefore, it is essential to forecast reliable air transportation demand with adequate methods. However, most of the air transportation demand models in Korea has been developed by simple regression analysis with several dummy variables. Simple regression analysis without considering stationarity of time-series data can bring spurious outputs when a direct causal relationship between explanatory variables and dependent variable does not exist. In this paper, econometric model were developed for air transportation demand based on stationarity in time-series data. Unit root test and co-integration test are used for testing hypothesis of stationarity.

• The Journal of Bigdata
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• v.8 no.1
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• pp.23-34
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• 2023

Nowadays, the Designated Driver Services employ dynamic pricing, which adapts in real-time based on nearby driver availability, service user volume, and current weather conditions during the user's request. The uncertain volatility is the main cause of price increases, leading to customer attrition and service refusal from driver. To make a good Designated Driver Services, development of a demand forecasting model is required. In this study, we propose developing a demand forecasting model using data from the Designated Driver Service by considering normal and peak periods, such as rush hour and rush day, as prior knowledge to enhance the model performance. We propose a new methodology called Time-Series with Conditional Probability(TSCP), which combines conditional probability and time-series models to enhance performance. Extensive experiments have been conducted with real Designated Driver Service data, and the result demonstrated that our method outperforms the existing time-series models such as SARIMA, Prophet. Therefore, our study can be considered for decision-making to facilitate proactive response in Designated Driver Services.