• 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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.3
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• pp.144-151
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• 2005

This paper presents a forecasting technique of the short-term marginal price (SMP) using an Artificial Neural Network (ANN). The SW forecasting is a very important element in an electricity market for the optimal biddings of market participants as well as for market stabilization of regulatory bodies. Input data are organized in two different approaches, time-axis and day-axis approaches, and the resulting patterns are used to train the ANN. Performances of the two approaches are compared and the better estimate is selected by a composition rule to forecast the SMP. By combining the two approaches, the proposed composition technique reflects the characteristics of hourly, daily and seasonal variations, as well as the condition of sudden changes in the spot market, and thus improves the accuracy of forecasting. The proposed method is applied to the historical real-world data from the Korea Power Exchange (KPX) to verify the effectiveness of the technique.

• 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).

• Journal of Information Technology Applications and Management
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• v.19 no.1
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• pp.1-12
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• 2012

Data mining is the process of searching and analyzing large quantities of data for finding out meaningful patterns and rules. Artificial Neural Network (ANN) is one of the tools of data mining which is becoming very popular in forecasting the future values. Some of the areas where it is used are banking, medicine, retailing and fraud detection. In finance, artificial neural network is used in various disciplines including stock market forecasting. In the stock market time series, due to high volatility, it is very important to choose a model which reads volatility and forecasts the future values considering volatility as one of the major attributes for forecasting. In this paper, an attempt is made to develop two models - one using feed forward back propagation Artificial Neural Network and the other using Autoregressive Conditional Heteroskedasticity (ARCH) technique for forecasting stock market returns. Various parameters which are considered for the design of optimal ANN model development are input and output data normalization, transfer function and neuron/s at input, hidden and output layers, number of hidden layers, values with respect to momentum, learning rate and error tolerance. Simulations have been done using prices of daily close of Sensex. Stock market returns are chosen as input data and output is the forecasted return. Simulations of the Model have been done using MATLAB$^{(R)}$ 6.1.0.450 and EViews 4.1. Convergence and performance of models have been evaluated on the basis of the simulation results. Performance evaluation is done on the basis of the errors calculated between the actual and predicted values.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.8 no.4
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• pp.157-162
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• 2009

This study's purpose is to forecast the market demand of UVS (u-Transportation Vehicle Sensor) OBU (On-board Unit) of the ubiquitous Transportation. Bass model, Logistic model, and Gompertz model were used for the forecasting market demand. Firstly, this research focused on the market size for the u-T OBU. All three models were used for the market size prediction and the average values were used. The Bass model were calibrated and the market demand for the UVS OBU of the u-Transportation system were estimated using this model.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.2
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• pp.149-154
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• 2013

Short term load forecasting for electric power demand is essential for stable power system operation and efficient power market operation. High accuracy of the short term load forecasting can keep the power system more stable and save the power market operation cost. We propose an optimal coefficient selection method for exponential smoothing model in short term load forecasting on weekdays. In order to find the optimal coefficient of exponential smoothing model, load forecasting errors are minimized for actual electric load demand data of last three years. The proposed method are verified by case studies for last three years from 2009 to 2011. The results of case studies show that the average percentage errors of the proposed load forecasting method are improved comparing with errors of the previous methods.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_2
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• pp.715-724
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• 2023

Solar energy forecasting is essential for (1) power system planning, management, and operation, requiring accurate predictions. It is crucial for (2) ensuring a continuous and sustainable power supply to customers and (3) optimizing the operation and control of renewable energy systems and the electricity market. Recently, research has been focusing on developing solar energy forecasting models that can provide daily plans for power usage and production and be verified in the electricity market. In these prediction models, various data, including solar energy generation and climate data, are chosen to be utilized in the forecasting process. The most commonly used climate data (such as temperature, relative humidity, precipitation, solar radiation, and wind speed) significantly influence the fluctuations in solar energy generation based on weather conditions. Therefore, this paper proposes a hybrid forecasting model by combining the strengths of the Prophet model and the GRU model, which exhibits excellent predictive performance. The forecasting periods for solar energy generation are tested in short-term (2 days, 7 days) and medium-term (15 days, 30 days) scenarios. The experimental results demonstrate that the proposed approach outperforms the conventional Prophet model by more than twice in terms of Root Mean Square Error (RMSE) and surpasses the modified GRU model by more than 1.5 times, showcasing superior performance.

• Korean Management Science Review
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• v.19 no.2
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• pp.125-137
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• 2002

The telecommunications market is expanding rapidly and becoming more substitutive. In this environment, demand forecasting is very difficult, yet important for both practitioners and researchers. in this paper, we adopt the modeling approach proposed dy Jun and Park ［6］. The basic premise is that demand patterns result from choice behavior, where customers choose a product to maximize their utility. We apply a choice-based substitutive diffusion model to the Korean mobile telecommunication service market where digital service has completely replaced analog service. In comparison with Bass-type multigeneration models. our model provides superior fitting and forecasting performance. The choice-based model is useful in that it enables the description of such complicated environments and provides the flexibility to include marketing mix variables such as price and advertising in the regression analysis.

• Korean System Dynamics Review
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• v.5 no.2
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• pp.67-88
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• 2004

This paper attempts to explore the generic pitfalls of the traditional number-crunching methods adopted thus far for the forecast of newly emerging market trends, and present an alternative by introducingsystems thinking to the portable Internet service market as an example, followed by its rationale as a new tool for forecasting and some reasoning about why traditional methods are no longer appropriate. Most adoption models in general to forecast market trends have several limitations due to theirbasic assumptions and prospective. First, they fail to capture dynamic interactions among the factors involved over time, with implicit assumptions of 'unilateral causality' in that each factor contributes as a cause to the effect, i.e., causality runs one way; each factor acts independently the weighting factor of each is fixed, etc. Second, the number-crunching models have no way of taking into account the impact of delayed feedback often caused by introducing new policies and legislative changes on the whole system under investigation. Third, there is not a way to reflect the effect of competition by players.

• The Journal of Fisheries Business Administration
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• v.39 no.1
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• pp.17-42
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• 2008

This study tried to build a structure model for the Korean flatfish aquaculture market by a system dynamics approach. A pool of several factors to influence the market structure was built. In addition, several reasonable factors related to the flatfish aquaculture market were selected to construct the causal loop diagram (CLD). Then the related stock/flow diagrams of the causal loop diagrams were constructed. This study had been forecasting a production price and supply, demand, and consumption volume for the flatfish market by a monthly basis, and then made some validation to the forecasting. Finally, four governmental policies such as import, storage, reduction of input, and demand control were tentatively evaluated by the created model. As a result, the facts that the demand control policy is most effective, and import and storage policies are moderately effective were found.