• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2011년도 학술발표회
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• pp.473-473
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• 2011

Group method of data handling neural networks model (GMDH-NNM) is used to estimate daily reference evapotranspiration (ETo) using limited climatic variables such as max temperature ($T_{max}$), min temperature ($T_{min}$), mean wind speed ($W_{mean}$), mean relative humidity ($RH_{mean}$) and sunshine duration (SD). And, for the performances of GMDH-NNM, it consists of training and test performances, respectively. The training and test performances are carried out using daily time series data, respectively. From this research, we evaluate the impact of GMDH-NNM for the modeling of the nonlinear time series data. We should, thus, construct the credible data of the daily ETo data using GMDH-NNM, and can suggest the methodology for the irrigation and drainage networks system. Furthermore, this research represents that the strong nonlinear relationship such as ETo modeling can be generalized using GMDH-NNM.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2010년도 학술발표회
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• pp.1596-1599
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• 2010

Group method of data handling neural networks model (GMDH-NNM) is used to estimate daily pan evaporation (PE) using limited climatic variables such as max temperature ($T_{max}$), min temperature ($T_{min}$), mean wind speed ($W_{mean}$), mean relative humidity ($RH_{mean}$) and sunshine duration (SD). And, for the performances of GMDH-NNM, it is composed of training and test performances, respectively. The training and test performances are carried out using daily time series data, respectively. From this research, we evaluate the impact of GMDH-NNM for the modeling of the nonlinear time series data. We should, thus, construct the credible data of the daily PE data using GMDH-NNM, and can suggest the methodology for the irrigation and drainage networks system. Furthermore, this research represents that the strong nonlinear relationship such as pan evaporation modeling can be generalized using GMDH-NNM.

• 상하수도학회지
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• 제12권4호
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• pp.43-52
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• 1998

The purpose of this study is to establish a method of estimating the daily urban water demend using Backpropagation algorithm is part of ANN(Artificial Neural Network). This method will be used for the development of the efficient management and operations of the water supply facilities. The data used were the daily urban water demend, the population and weather conditions such as treperarture, precipitation, relative humidity, etc. Kwangju city was selected for the case study area. We adjusted the weights of ANN that are iterated the training data patterns. We normalized the non-stationary time series data [-1,+1] to fast converge, and choose the input patterns by statistical methods. We separated the training and checking patterns form input date patterns. The performance of ANN is compared with multiple-regression method. We discussed the representation ability the model building process and the applicability of ANN approach for the daily water demand. ANN provided the reasonable results for time series forecasting.

• 한국정보시스템학회지:정보시스템연구
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• 제31권3호
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• pp.197-220
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• 2022

Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV²) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.

• Journal of the Korean Data and Information Science Society
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• 제23권6호
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• pp.1309-1317
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• 2012

Stochastic weather generators are commonly used to simulate time series of daily weather, especially precipitation amount. Recently, a generalized linear model (GLM) has been proposed as a convenient approach to fitting these weather generators. In this paper, a stochastic weather generator is considered to model the time series of daily precipitation at Seoul in South Korea. As a covariate, global temperature is introduced to relate long-term temporal scale predictor to short-term temporal predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate time series of seasonal total precipitation in the GLM weather generator as covariates. It is veri ed that the addition of these covariates does not distort the performance of the weather generator in other respects.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 추계학술대회
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• pp.151-152
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• 2005

IGARCH and Stochastic Volatility Model(SVM, for short) have frequently provided useful approximations to the real aspects of financial time series. This article is concerned with modeling various Korean financial time series using both IGARCH and Stochastic Volatility Models. Daily data sets with sample period ranging from 2000 and 2004 including KOSPI, KOSDAQ and won-dollar exchange rate are comparatively analyzed using IGARCH and SVM.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.835-841
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• 2005

IGARCH and Stochastic Volatility Model(SVM, for short) have frequently provided useful approximations to the real aspects of financial time series. This article is concerned with modeling various Korean financial time series using both IGARCH and stochastic volatility models. Daily data sets with sample period ranging from 2000 and 2004 including KOSPI, KOSDAQ and won-dollar exchange rate are comparatively analyzed using IGARCH and SVM.

• Journal of the Korean Data and Information Science Society
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• 제16권2호
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• pp.283-288
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• 2005

Value at Risk(VaR) has been proven useful in finance literature as a tool of risk management(cf. Jorion(2001)). This article is concerned with introducing VaR to various Korean financial time series. Five daily data sets with sample period ranging from 2000 and 2004 such as KOSPI, KOSPI 200, KOSDAQ, KOSDAQ 50 and won-dollar exchange rate are analyzed using GARCH modeling and in turn VaR is obtained for each data.

• Water Engineering Research
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• 제6권2호
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• pp.39-48
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• 2005

The Brazos River is one of the longest rivers contained entirely in the state of Texas, flowing over 700 miles from northwest Texas to the Gulf of Mexico. Today, the Brazos River Authority and Texas Commission on Environmental Quality interest in drought protection plan, waterpower project, and allowing the appropriation of water system-wide and water right within the Brazos River Basin to meet water needs of customers like farmers and local civilians in the future. Especially, this purpose of this paper primarily intended to provide the data for the engineering guidelines and make easily geological mapping tool. In the Brazos River basin, many stream-flow gage station sites are not working, and they can not provide stream-flow data sets enough for development of the Probable Maximum Flood (PMF) for use in the evaluation of proposed and existing dams and other impounding structures. Integrated GIS-Spatial Interpolation (GIS-SI) tool are composed of two parts; (1) extended GIS technique (new making interface for hydrological regionalization parameters plus classical GIS mapping skills), (2) Spatial Interpolation technique using weighting factors from kriging method. They are obtained from the relationship among location and elevation of geological watershed and existing stream-flow datasets. GIS-SI technique is easily used to compute parameters which get drainage areas, mean daily/monthly/annual precipitation, and weighted values. Also, they are independent variables of multiple linear regressions for simulation at un gaged stream-flow sites. In this study, GIS-SI technique is applied to the Brazos river basin in Texas. By assuming the ungaged flow at the sites of Palo Pinto, Bryan and Needville, the simulated daily/monthly/annual time series are compared with observed time series. The simulated daily/monthly/annual time series are highly correlated with and well fitted to the observed times series.

• Communications for Statistical Applications and Methods
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• 제28권4호
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• pp.329-338
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• 2021

Deep learning methods have been developed, used in various fields, and they have shown outstanding performances in many cases. Many studies predicted a daily stock return, a classic example of time-series data, using deep learning methods. We also tried to apply deep learning methods to Korea's stock market data. We used Korea's stock market index (KOSPI) and several individual stocks to forecast daily returns and directions. We compared several deep learning models with other machine learning methods, including random forest and XGBoost. In regression, long short term memory (LSTM) and gated recurrent unit (GRU) models are better than other prediction models. For the classification applications, there is no clear winner. However, even the best deep learning models cannot predict significantly better than the simple base model. We believe that it is challenging to predict daily stock return data even if we use the latest deep learning methods.