• 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.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.65-76
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• 2014

Housing prices are influenced by external shock factors such as real estate policy or economy. Thus, the intervention effect is important for the development of forecasting model for housing price index. In this paper, we examined the degree of effective power of external shock factors for forecasting housing price index and analyzed time series models for efficient forecasting of housing price index. It is shown that intervention models are better than other models in forecasting results using real data based on the accuracy criteria.

• Journal of Environmental Science International
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• v.8 no.3
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• pp.349-354
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• 1999

The transfer function was introduced to establish the prediction method for the DO concentration at the intaking point of Kongju Water Works System. In the mose cases we analyze a single time series without explicitly using information contained in the related time series. In many forecasting situations, other events will systematically influence the series to be forecasted(the dependent variables), and therefore, there is need to go beyond a univariate forecasting model. Thus, we must bulid a forecasting model that incorporates more than one time series and introduces explicitly the dynamic characteristics of the system. Such a model is called a multiple time series model or transfer function model. The purpose of this study is to develop the stochastic stream water quality model for the intaking station of Kongju city waterworks in Keum river system. The performance of the multiplicative ARIMA model and the transfer function noise model were examined through comparisons between the historical and generated monthly dissolved oxygen series. The result reveal that the transfer function noise model lead to the improved accuracy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.852-856
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• 2015

Demand side management (DSM) program has been frequently used for reducing the system peak load because it gives utilities and independent system operator (ISO) a convenient way to control and change amount of electric usage of end-use customer. Planning and operating methods are needed to efficiently manage a DSM program. This paper presents a planning method for DSM program. A planning method for DSM program should include an electric load forecasting, because this is the most important factor in determining how much to reduce electric load. In this paper, load forecasting with the temperature stochastic modeling and the sensitivity to temperature of the electric load is used for improving load forecasting accuracy. The proposed planning method can also estimate the required day, hour and total capacity of DSM program using Monte-Carlo simulation. The results of case studies are presented to show the effectiveness of the proposed planning method.

• Proceedings of the KIEE Conference
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• 2005.11b
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• pp.148-150
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• 2005

Since the SMP(System Marginal Price) is a vital factor to the market participants who intend to maximize the their profit and to the ISO(Independent System Operator) who wish to operate the electricity market in a stable sense, the short-term marginal price forecasting should be performed correctly. This paper presents a methodology of a day-ahead SMP forecasting using ARIMA(Autoregressive Integrated Moving Average) based on the Time Series. And also we suggested a correction algorithm to minimize the forecasting error in order to improve efficiency and accuracy of the SMP forecasting. To show the efficiency and effectiveness of the proposed method, the numerical studies have been performed using Historical data of SMP in 2004 published by KPX(Korea Power Exchange).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1088-1095
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• 2015

In most cases, the use of electric power is associated with the economic scale of a nation closely. Thus, the electric power load forecasting plays an important role for the national economic plan. This paper deals with the design method for the electric power load forecasting system. In this paper, RCR-MA data processing, which can make the complex properties of the original data form simple, is proposed. Next, IT2TSK FLS, which can reflect the uncertainty of data more than T1TSK FLS, is applied. Consequently, the structural advantage of the proposed system can improve the forecasting accuracy, and is verified by using two types of electric power data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.12
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• pp.1657-1660
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• 2013

Short-term load forecasting for Chusok and New Year's consecutive holidays is very difficult, due to the irregular characteristics compared with ordinary weekdays and insufficient holidays historical data. During consecutive holidays of New Year and Chusok, most of industries reduce their operation rates and their electrical load levels. The correlation between businesses' operation rates and their loads during consecutive holidays of New Year and Chusok is analysed and short-term load forecasting algorithm for consecutive holidays considering businesses' operation rates of industries is proposed. Test results show that the proposed method improves the accuracy of short-term load forecasting over fuzzy linear regression method.

• Proceedings of the Korean Association for Survey Research Conference
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• 2000.06a
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• pp.15-34
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• 2000

Since the results of the election forecasting survey that was executed jointly by T.V. stations and survey research companies in the 16th Korea General Election(April 13, 2000) had many errors, the reliability of the election forecasting survey was greatly damaged. Therefore, in order to recover the reliability and to increase the accuracy of the election forecasting survey I the future, we figure out the sources of the survey\\`s errors and suggest methods of reducing them through deeply analyzing the forecasting data from many angles. In addition, we discuss some problems and an improvable direction on exit poll executed for the first time.

• Korean System Dynamics Review
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• v.10 no.1
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• pp.61-76
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• 2009

We try to use system dynamics to forecast the demand/supply and price, also transportation fare for iron ore. Iron ore is very important mineral resource for industrial production. The structure for this system dynamics shows non-linear pattern and we anticipated the system dynamic method will catch this non-linear reality better than the regression analysis. Our model is calibrated and tested for the past 6 year monthly data (2003-2008) and used for next 6 year monthly data(2008-2013) forecasting. The test results show that our system dynamics approach fits the real data with higher accuracy than the regression one. And we have run the simulations for scenarios made by possible future changes in demand or supply and fare related variables. This simulations imply some meaningful price and fare change patterns.

• KIEE International Transactions on Power Engineering
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• v.4A no.3
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• pp.159-166
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• 2004

Forecasting prices in electricity markets is critical for consumers and producers in planning their operations and managing their price risk. We utilize the generalized autoregressive conditionally heteroskedastic (GARCH) method to forecast the electricity prices in two regions of New York： New York City and Central New York State. We contrast the one-day forecasts of the GARCH against techniques such as dynamic regression, transfer function models, and exponential smoothing. We also examine the effect on our forecasting of omitting some of the extreme values in the electricity prices. We show that accounting for the extreme values and the heteroskedactic variance in the electricity price time-series can significantly improve the accuracy of the forecasting. Additionally, we document the higher volatility in New York City electricity prices. Differences in volatility between regions are important in the pricing of electricity options and for analyzing market performance.