• Journal of Korea Water Resources Association
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• v.37 no.1
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• pp.77-86
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• 2004

This paper presents a reservoir operation plan coupled with storage forecasting model to maintain a target storage and a critical storage. The observed storage data from 1990 to 2001 in the Geum-Gang agricultural reservoir in Korea have been applied to the low flow frequency analysis, which yields storage for each return period. Two year return period drought storage is then designated as the target storage and ten year return period drought storage as the critical storage. Storage in reservoir should be forecasted to perform reasonable reservoir operation. The predicted storage can be effectively utilized to establish a reservoir operation plan. In this study the autoregressive error (ARE) model and the ARIMA model are adopted to predict storage of reservoir. The ARIMA model poorly generated reservoir storage in series because only observed storage data were used, but the autoregressive error model made to enhance the reliability of the forecasted storage by applying the explanation variables to the model. Since storages of agricultural reservoir with respect to time have been affected by irrigation area, high or mean temperature, precipitation, previous storage and wind velocity, the autoregressive error model has been adopted to analyze the relationship between storage at a period and affecting factors for storage at the period. Since the equation for predicting storage at a period by the autoregressive error model is similar to the continuity equation, the predicting storage equation may be practical. The results from compared the actual storage in 2002 and the predicted storage in the Geum-Gang reservoir show that forecasted storage by the autoregressive error model is reasonable.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.343-358
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• 2006

The distributional properties of forecasts in an integer-valued time series model have not been discovered yet mainly because of the complexity arising from the binomial thinning operator. We propose two bootstrap methods to obtain nonparametric prediction intervals for an integer-valued autoregressive model : one accommodates the variation of estimating parameters and the other does not. Contrary to the results of the continuous ARMA model, we show that the latter is better than the former in forecasting the future values of the integer-valued autoregressive model.

• Korean Journal of Culture and Social Issue
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• v.16 no.4
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• pp.447-468
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• 2010

The purpose of this study was to attain a clearer understanding of the reciprocal effects of deviant self-concept and delinquent behaviors by applying a latent state-trait autoregressive modeling approach. Although traditional autoregressive cross-lagged (ARCL) modeling has been widely applied to test the longitudinal reciprocal relationship between the two constructs, it could produce misspecified findings if there were trait-like processes involved in this relationship. The latent state-trait autoregressive(LST-AR) modeling was applied to control trait effects of deviant self-concept and to examine the reciprocal causal relations between the two constructs. Data were taken from a sample of 3,449 eighth graders who were followed annually for 5 years from the Korea Youth Panel Study. The combining LST-AR model with ARCL model substantiated the reciprocal effects of deviant self-concept and delinquent behaviors, even after the stable trait component of deviant self-concept was taken into account. The present findings shed lights on the reciprocal effects of behaviors (i.e., delinquency) and self concepts (i.e., deviant self-concept). Not only did behaviors change corresponding self-concept, but the ways adolescents perceived themselves influenced their behaviors.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.133-144
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• 2016

This paper proposes a parsimonious periodic autoregressive (PAR) model. The proposed model performance is evaluated through an analysis of Korean unemployment rate series that is compared with existing models. We exploit some common features among each seasonality and confirm it by LR test for the parsimonious PAR model in order to impose a parsimonious structure on the PAR model. We observe that the PAR model tends to be superior to existing seasonal time series models in mid- and long-term forecasts. The proposed parsimonious model significantly improves forecasting performance.

• Journal of the Korean Society of Safety
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• v.16 no.1
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• pp.88-93
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• 2001

This study discusses frequency analysis based on autoregressive (AR) time series model, and process characterization in orthogonal cutting of a fiber-matrix composite materials. A sparsely distributed idealized composite material, namely a glass reinforced polyester (GFRP) was used as workpiece. Analysis method employs a force sensor and the signals from the sensor are processed using AR time series model. The resulting pattern vectors of AR coefficients are then passed to the feature extraction block. Inside the feature extraction block, only those features that are most sensitive to different types of cutting mechanisms are selected. The experimental correlations between the different chip formation mechanisms and AR model coefficients are established.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.589-596
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• 2011

The conditional autoregressive value at risk (CAViaR) model is useful for risk management, which does not require the assumption that the conditional distribution does not vary over time but the volatility does. But it does not provide volatility forecasts, which are needed for several important applications such as option pricing and portfolio management. For a variety of probability distributions, it is known that there is a constant relationship between the standard deviation and the distance between symmetric quantiles in the tails of the distribution. This inspires us to use a support vector quantile regression (SVQR) for volatility forecasts with the distance between CAViaR forecasts of symmetric quantiles. Simulated example and real example are provided to indicate the usefulness of proposed forecasting method for volatility.

• Communications for Statistical Applications and Methods
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• v.3 no.2
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• pp.77-83
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• 1996

This paper is concerned with an estimation problem for the AR(1) process $Y_t, t=0, {\pm}1, {\cdots}$with time carying autoregressive coefficient, where coefficient itself is also stochastic process. Attention is directed to the problem of finding a consistent estimator of ${\Phi}$, the mean level of autoregressive coefficient. The asymptotic distribution of the resulting consistent estimator of ${\Phi}$, is them discussed. We do not assume any time series model for the time varying autoregressive coefficient.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.831-839
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• 2010

Least squares support vector machine (LS-SVM) is a kernel trick gaining a lot of popularities in the regression and classification problems. We use LS-SVM to propose a iterative algorithm for a nonlinear generalized autoregressive conditional heteroscedasticity model in the mean (GARCH-M) model to estimate the mean and the conditional volatility of stock market returns. The proposed method combines a weighted LS-SVM for the mean and unweighted LS-SVM for the conditional volatility. In this paper, we show that nonlinear GARCH-M models have a higher performance than the linear GARCH model and the linear GARCH-M model via real data estimations.

• Journal of Soil and Groundwater Environment
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• v.19 no.4
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• pp.23-30
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• 2014

Coastal aquifer in Muan, Jeonnam, has experienced heavy seawater intrusion caused by the extraction of a substantial amount of groundwater for the agricultural purpose throughout the year. It was observed that groundwater level dropped below sea level due to heavy pumping during a dry season, which could accelerate seawater intrusion. Therefore, water level needs to be monitored and managed to prevent further seawater intrusion. The purpose of this study is to develop the autoregressive-cross-regressive (ARCR) models that can predict the present or future groundwater level using its own previous values and pumping events. The ARCR model with pumping and water level data of the proceeding five hours (i.e., the model order of five) predicted groundwater level better than that of the model orders of ten and twenty. This was contrary to expectation that higher orders do increase the coefficient of determination ($R^2$) as a measure of the model's goodness. It was found that the ARCR model with order five was found to make a good prediction of next 48 hour groundwater levels after the start of pumping with $R^2$ higher than 0.9.

• Korean Journal of Agricultural Science
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• v.45 no.4
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• pp.859-870
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• 2018

Dried red peppers are a staple agricultural product used in Korean cuisine and as such, are an important aspect of agricultural producers' income. Correctly forecasting both their supply and demand situations and price is very important in terms of the producers' income and consumer price stability. The primary objective of this study was to compare the performance of time series forecasting models for dried red peppers in Korea. In this study, three models (an autoregressive model with exogenous variables [ARX], AR-exponential generalized autoregressive conditional heteroscedasticity [EGARCH], and ARX-EGARCH) are presented for forecasting the wholesale price of dried red peppers. As a result of the analysis, it was shown that the ARX model and ARX-EGARCH model, each of which adopt both the rolling window and the adding approach and use the agricultural cooperatives price as the exogenous variable, showed a better forecasting performance compared to the autoregressive model (AR)-EGARCH model. Based on the estimation methods and results, there was no significant difference in the accuracy of the estimation between the rolling window and adding approach. In the case of dried red peppers, there is limitation in building the price forecasting models with a market-structured approach. In this regard, estimating a forecasting model using only price data and identifying the forecast performance can be expected to complement the current pricing forecast model which relies on market shipments.