• 설비공학논문집
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• 제25권6호
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• pp.310-316
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• 2013

This study was designed to investigate a method for short-term, real-time energy demand prediction, to cope with changing loads for the effective operation and management of buildings. Through a case study, a novel methodology for real-time energy demand prediction with the use of weather forecasting data was suggested. To perform the input and output operations of weather data, and to calculate solar radiation and EnergyPlus, the BCVTB (Building Control Virtual Test Bed) was designed. Through the BCVTB, energy demand prediction for the next 24 hours was carried out, based on 4 real-time weather data and 2 solar radiation calculations. The weather parameters used in a model equation to calculate solar radiation were sourced from the weather data of the KMA (Korea Meteorological Administration). Depending on the local weather forecast data, the results showed their corresponding predicted values. Thus, this methodology was successfully applicable to anywhere that local weather forecast data is available.

• The Journal of Asian Finance, Economics and Business
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• 제8권8호
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• pp.647-658
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• 2021

Analysts' forecasts are important for providing useful guidance to investors, especially individual or small investors, and therefore it becomes critical to identify the elements which can potentially increase errors in analysts' forecasts. This study investigates potential factors which can lead to errors in forecasting by analysts, specifically in terms of the level and attributes of corporate earnings. Utilizing a sample of firms listed on the Korean stock markets, this study provides evidence that firms with more volatile and unpredictable earnings feature less accurate analyst forecasts. This study fills a void in the literature by conducting empirical tests for earnings attributes in terms of volatility and unpredictability that could potentially undermine the forecast accuracy. The negative association between the quality of earnings and forecast accuracy is more pronounced for firms with negative net income values. Additional analysis demonstrates that forecast accuracy is significantly lower for the fourth quarter than for other fiscal quarters and that fourth quarter earnings tend to be more volatile and unpredictable. This study contributes to the literature by providing new empirical evidence regarding the comprehensive effects of earnings quality and level on analysts' forecasting accuracy and further suggests potential factors contributing to the fourth quarter anomaly in analyst forecasts in terms of earnings attributes.

• International Journal of Advanced Culture Technology
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• 제2권2호
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• pp.11-14
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• 2014

In the smart grid, it will be possible to communicate with the consumers for the purposes of monitoring and controlling their power consumption without disturbing their business or comfort. This will bring easier administration capabilities for the utilities. On the other hand, consumers will require more advanced home automation tools which can be implemented by using advanced sensor technologies. For instance, consumers may need to adapt their consumption according to the dynamically varying electricity prices which necessitates home automation tools. This paper tries to combine neural network and nonlinear autoregressive with exogenous variable (NARX) class for next week electric load forecasting. The suitability of the proposed approach is illustrated through an application to electric load consumption data. The suggested system provides a useful and suitable tool especially for the load forecasting.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2003년도 추계학술대회
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• pp.57-72
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• 2003

This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.

• 농업과학연구
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• 제41권1호
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• pp.23-27
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• 2014

Potato late blight occurrence is caused by various environmental factors, and the progress can be regularly predicted so that several predictive models have been developed. The models predict the timing of the disease occurrence, but they do not include the methods of the disease control. Effective fungicide control, economic threshold, prediction models were investigated in the study to reflect on customized control modules for the model forecasting the occurrence of potato late blight.

• 농업과학연구
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• 제41권1호
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• pp.29-34
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• 2014

Phytophthora blight occurrence is caused by various environmental factors, and the progress can be regularly predicted so that several predictive models have been developed. The models predict the timing of the disease occurrence, but they do not include the methods of the disease control. Effective fungicide control, control threshold, prediction models were investigated in the study to reflect on customized control modules for the model forecasting the occurrence of Phytophthora blight on hot pepper.

• 응용통계연구
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• 제29권1호
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• pp.145-155
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• 2016

이 논문에서는 기상에 따른 고령 환자의 질병 발생빈도를 예측하는 방법을 비교한다. 분석을 위해 건강보험심사평가원의 고령 환자 의료 자료와 기상청 자료를 주별, 권역별로 병합한다. 기상에 영향을 받는 질병의 주별 발생 빈도를 ARMAX모형, VARMAX모형, TSCS회귀모형으로 분석하고 MSE, MAPE, MAE 기준으로 모형을 비교했다.

• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.210-218
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• 2023

The author presents a simple data-driven intraday technical indicator trading approach based on Genetic Programming (GP) for return forecasting in the Bitcoin market. We use five trend-following technical indicators as input to GP for developing trading rules. Using data on daily Bitcoin historical prices from January 2017 to February 2020, our principal results show that the combination of technical analysis indicators and Artificial Intelligence (AI) techniques, primarily GP, is a potential forecasting tool for Bitcoin prices, even outperforming the buy-and-hold strategy. Sensitivity analysis is employed to adjust the number and values of variables, activation functions, and fitness functions of the GP-based system to verify our approach's robustness.

• 산업경영시스템학회지
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• 제46권2호
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• pp.102-108
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• 2023

Recently in Korea, YouTube stock channels increased rapidly due to the high social interest in the stock market during the COVID-19 period. Accordingly, the role of new media channels such as YouTube is attracting attention in the process of generating and disseminating market information. Nevertheless, prior studies on the market forecasting power of YouTube stock channels remain insignificant. In this study, the market forecasting power of the information from the YouTube stock channel was examined and compared with traditional news media. To measure information from each YouTube stock channel and news media, positive and negative opinions were extracted. As a result of the analysis, opinion in channels operated by media outlets were found to be leading indicators of KOSPI market returns among YouTube stock channels. The prediction accuracy by using logistic regression model show 74%. On the other hand, Sampro TV, a popular YouTube stock channel, and the traditional news media simply reported the market situation of the day or instead showed a tendency to lag behind the market. This study is differentiated from previous studies in that it verified the market predictive power of the information provided by the YouTube stock channel, which has recently shown a growing trend in Korea. In the future, the results of advanced analysis can be confirmed by expanding the research results for individual stocks.

• 한국경영과학회지
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• 제28권3호
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• pp.81-101
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• 2003

The financial statements purpose to provide useful information to decision-making process of business managers. The value-relevant information, however, embedded in the financial statement has been often overlooked in Korea. In fact, the financial statements in Korea have been utilized for nothing but account reports to Security Supervision Boards (SSB). The objective of this study is to develop earnings forecasting models through financial statement analysis using artificial intelligence (AI). AI methods are employed in forecasting earnings: artificial neural networks (ANN) for manufacturing industry and case~based reasoning (CBR) for banking industry. The experimental results using such AI methods are as follows. Using ANN for manufacturing industry records 63.2% of hit ratio for out-of-sample, which outperforms the logistic regression by around 4%. The experiment through CBR for banking industry shows 65.0% of hit ratio that beats the statistical method by 13.2% in holdout sample. Finally, the prediction results for manufacturing industry are validated through monitoring the shift in cumulative returns of portfolios based on the earning prediction. The portfolio with the firms whose earnings are predicted to increase is designated as best portfolio and the portfolio with the earnings-decreasing firms as worst portfolio. The difference between two portfolios is about 3% of cumulative abnormal return on average. Consequently, this result showed that the financial statements in Korea contain the value-relevant information that is not reflected in stock prices.