• Proceedings of the Korean Society of Computer Information Conference
• /
• 2022.07a
• /
• pp.37-38
• /
• 2022

농산물의 산지 가격이나 도매가격이 등락하면, 즉시 또는 일정한 시차 이후에 소비자가격도 등락한다. 본 논문에서는 선형회귀모델을 통해 쌀 가격을 예측하고 쌀 가격에 영향을 미치는 날씨의 시기를 찾아보고자 한다. 이에 따라 KAMIS, 기상자료개방포털, KOSIS에서 수집한 날씨, 생산량, 그리고 소비자물가 등락률 데이터를 이용하여 쌀 가격 예측을 수행하고, 날씨 데이터와 쌀 가격 데이터의 날짜 간격을 두어 날씨가 쌀 가격에 영향을 미치는 시기를 알아보았다. 모델 평가 결과, 2개월 간격을 두고 예측한 RMSE가 164.135로 가장 큰 영향을 미쳤다. 본 연구를 기반으로 향후 다른 농산물의 가격 예측도 가능할 것이며 농산물에 영향을 미치는 변수의 시기도 예측할 수 있을 것으로 기대한다.

• Proceedings of the Korea Database Society Conference
• /
• 1999.06a
• /
• pp.291-300
• /
• 1999

본 논문은 인공신경망과 귀납적 학습방법 등의 인공지능 방법과 선물가격결정에 대한 기존 재무이론을 사용하여 일상어취로 표현되는 파생상품 가격예측 모형을 개발하는데 있다. 모형의 개발은 1단계로 인공신경망이나 기존의 선물가격결정이론(평균보 유비용모형이나 일반균형모형)을 이용하여 선물 가격을 예측한 후, 서로 비교 분석하여 인공신경망 모형의 우수성을 확인하였다. 귀납적 학습방법중 CART 알고리듬을 사용하여 If-Then 규칙을 생성하였다. 특히 실용적 측면에서 선물가격의 일상어휘화를 통한 모형개발을 여러 가지 방법으로 시도하였다. 이러한 선물가격 예측모형의 유용성은 일단 If-Then 규칙으로 표현되어 전문가의 판단에 확실한 이론적인 근거를 제시할 수 있는 장점이 있으며, 특히 의사결정지원시스템으로 활용화 될 경우 매우 유용한 근거자료로 활용될 수 있다. 이러한 선물가격 예측모형의 정확성은 분석표본과 검증표본으로 나누어 검증표본에서 세가지 기본모형(평균보유 비용모형, 일반균형모형, 인공신경망 모형)과 각 모형의 귀납적 학습방법 모형의 다른 3가지 어휘표현방법 3가지를 모형별로 비교 분석하였다. 분석결과 인공신경망모형은 상당한 예측력을 갖고 있는 것으로 판명되었으며, 특히 CART를 기반으로 한 일상어취 기반의 선물가격예측 모형은 예측력이 높은 것으로 나타났다.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 1999.03a
• /
• pp.291-300
• /
• 1999

본 논문은 인공신경망과 귀납적 학습방법 등의 인공지능 방법과 선물가격결정에 대한 기존 재무이론을 사용하여 일상어휘로 표현되는 파생상품 가격예측 모형을 개발하는데 있다. 모형의 개발은 1단계로 인공신경망이나 기존의 선물가격결정이론(평균보유비용모형이나 일반균형모형)을 이용하여 선물 가격을 예측한 후, 서로 비교분석하여 인공신경망 모형의 우수성을 확인하였다. 귀납적 학습방법중 CART 알고리듬을 사용하여 If-Then 규칙을 생성하였다. 특히 실용적 측면에서 선물가격의 일상어휘화를 통한 모형개발을 여러 가지 방법으로 시도하였다. 이러한 선물가격 예측모형의 유용성은 일단 If-Then 규칙으로 표현되어 전문가의 판단에 확실한 이론적인 근거를 제시할 수 있는 장점이 있으며, 특히 의사결정지원시스템으로 활용화 될 경우 매우 유용한 근거자료로 활용될 수 있다. 이러한 선물가격 예측모형은 정확성은 분석표본과 검증표본으로 나누어 검증표본에서 세가지 기본모형(평균보유비용모형, 일반균형모형, 인공신경망 모형)과 각 모형의 귀납적 학습방법 모형의 다른 3가지 어휘표현방법 3가지를 모형별로 비교 분석하였다. 분석결과 인공신경망모형은 상당한 예측력을 갖고 있는 것으로 판명되었으며, 특히 CART를 기반으로 한 일상어휘 기반의 선물가격예측 모형은 예측력이 높은 것으로 나타났다.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.38 no.4
• /
• pp.309-314
• /
• 2010

In this work, the helicopter price estimation formula from Harris & Scully is revised and updated for more accurate prediction of the current prices of helicopters which can be applied in the preliminary design stage. As many as 74 helicopter price and sizing data after the year 1997 are newly included to the out-dated coefficients in the cost estimation formula. In addition, the updated price estimation formula is added to Tishchenko's preliminary sizing method for price consideration in the preliminary design stage. Sikorsky's S-76C+ helicopter is used along with other various helicopters for comparison and validation of the sizing code as well as the new updated price estimation formula.

• The Korean Journal of Applied Statistics
• /
• v.30 no.2
• /
• pp.271-280
• /
• 2017

Real-estate values and related economics are often the first read newspaper category. We are concerned about the opinions of experts on the forecast for real estate prices. The Box-Jenkins ARIMA model is a commonly used statistical method to predict housing prices. In this article, we tried to predict housing prices by combining independent component analysis (ICA) in multivariate data analysis and the Box-Jenkins ARIMA model. The two independent components for both the selling price index and the long-term rental price index were extracted and used to predict the future values of both indices. In conclusion, it has been shown that the actual indices and the forecast indices using ICA are more comparable to the forecasts of the ARIMA model alone.

• Korean Journal of Poultry Science
• /
• v.39 no.3
• /
• pp.233-239
• /
• 2012

Public forecasts for broiler's prices such as Korea Rural Economic Institute (KREI) outlook information are important for producers and agribusiness decision makers in enhancing economic decision making. However, the KREI forecasts have not been fully evaluated so far. In this study agricultural outlook price forecasts for broiler are evaluated under accuracy-based measures and classification-based measures which test the ability to categorize price movements directionally or within a forecasted range. Agricultural outlook price forecasts for broiler are efficient but biased. In the aspect of the monthly direction of price change 59% of its forecasts over the sample period are correct, and actual prices fall within the forecasted range 32% of the time. Results suggest that it is necessary and meaningful for the agricultural outlook center to evaluate the current forecasting method and try to find an alternative method for improving the forecasting technique.

• Environmental and Resource Economics Review
• /
• v.14 no.1
• /
• pp.201-220
• /
• 2005

Recently, we have been witnessing new records of crude oil price hikes. One question which naturally arises would be the possibility and accuracy of forecasting crude oil prices. This study tries to answer the relative predictability of futures prices compared to the forecasts based on experts system. Using WTI crude oil spot and futures prices, this study performs simple statistical comparisons in forecasting accuracy and a formal test of differences in forecasting errors. According to statistical results, WTI crude oil futures market turns out to be equally efficient relative to EIA experts system. Consequently, WTI crude oil futures market could be utilized as a market-based tool for price forecasting and/or resource allocation for both of petroleum producers and consumers.

• Journal of the Korea Convergence Society
• /
• v.12 no.12
• /
• pp.83-91
• /
• 2021

For agricultural products, supply is irregular due to changes in meteorological conditions, and it has high price elasticity. For example, if the supply decreases by 10%, the price increases by 50%. Due to these fluctuations in the prices of agricultural products, the Korean government guarantees the safety of prices to producers through small merchants' auctions. However, when prices plummet due to overproduction, protection measures for producers are insufficient. Therefore, in this paper, we designed a business model that can be used in the electronic transaction system by predicting the price of agricultural products with an artificial intelligence algorithm. To this end, the trained model with the training pattern pairs and a predictive model was designed by applying ARIMA, SARIMA, RNN, and CNN. Finally, the agricultural product forecast price data was classified into short-term forecast and medium-term forecast and verified. As a result of verification, based on 2018 data, the actual price and predicted price showed an accuracy of 91.08%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2017.10a
• /
• pp.412-414
• /
• 2017

It is important to predict price of agricultural products accurately to government, local government, bodies in charge of agriculture. Production and shipping of agricultural products are affected by weather condition significantly. In this research, prediction model of a Chinese cabbage which is highly sensitive to weather condition is proposed using deep learning technique. After performance of proposed model and a model of previous research is compared, superiority of proposed model is proved.

• Journal of the Korea Society for Simulation
• /
• v.23 no.4
• /
• pp.1-8
• /
• 2014

Cloud computing helps big data processing to make various information using IT resources. The government has to start the RPS(Renewable Portfolio Standard) and induce the production of electricity using renewable energy equipment. And the government manages system to gather big data that is distributed geographically. The companies can purchase the REC(Renewable Energy Certificate) to other electricity generation companies to fill shortage among their duty from the system. Because of the RPS use voluntary competitive market in REC trade and the prices have the large variation, RPS is necessary to predict the equitable REC price using RPS big data. This paper proposed REC price prediction method base on fuzzy logic using the price trend and trading condition infra in REC market, that is modeled in cloud computing environment. Cloud computing helps to analyze correlation and variables that act on REC price within RPS big data and the analysis can be predict REC price by simulation. Fuzzy logic presents balanced REC average trading prices using the trading quantity and price. The model presents REC average trading price using the trading quantity and price and the method helps induce well-converged price in the long run in cloud computing environment.