• 경영과학
• /
• 제32권4호
• /
• pp.97-108
• /
• 2015

This study proposes a mathematical model to estimate the economic value of weather forecast service, among which the precipitation forecast service is focused. The value is calculated in terms of users' satisfaction or dissatisfaction resulted from the users' decisions made by using the precipitation probabilistic forecasts and thresholds. The satisfaction values can be quantified by the traditional value score model, which shows the scaled utility values relative to the perfect forecast information. This paper extends the value score concept to a collective value score model which is defined as a weighted sum of users' satisfaction based on threshold distribution in a group of the users. The proposed collective value score model is applied to the picnic scenario by using four hypothetical sets of probabilistic forecasts, i.e., under-confident, over-confident, under-forecast and over-forecast. The application results show that under-confident type of forecasts outperforms the others as a measure of the maximum collective value regardless of users' dissatisfaction patterns caused by two types of forecast errors, e.g., miss and false alarm.

• 산업경영시스템학회지
• /
• 제30권2호
• /
• pp.89-98
• /
• 2007

In this paper the economic value of weather forecasts is valuated for profit-oriented enterprise decision-making situations. Value is estimated in terms of monetary profits (or benefits) resulted from the forecast user's decision under the specific payoff structure, which is represented by a profit/loss ratio model combined with a decision function and a value score (VS). The forecast user determines a business-related decision based on the probabilistic forecast, the user's subjective reliability of the forecasts, and the payoff structure specific to the user's business environment. The VS curve for a meteorological forecast is specified by a function of the various profit/loss ratios, providing the scaled economic value relative to the value of a perfect forecast. The proposed valuation method based on the profit/loss ratio model and the VS is adapted for hypothetical sets of forecasts and verified for site-specific probability of precipitation forecast of 12 hour and 24 hour-lead time, which is generated from Korea meteorological administration (KMA). The application results show that forecast information with shorter lead time can provide the decision-makers with great benefits and there are ranges of profit/loss ratios in which high subjective reliability of the given forecast is preferred.

• 한국콘텐츠학회논문지
• /
• 제13권11호
• /
• pp.382-395
• /
• 2013

기상정보 제공자들은 예보사용자들이 제공되는 예보서비스에 얼마나 만족하고 있는지를 알고 싶어 한다. 더 나은 예보서비스 제공을 위해서 각국의 기상 커뮤니티들은 사용자들의 만족도에 관한 다양한 설문조사를 진행하고 있다. 하지만 대부분의 설문조사들이 단순하게 사용자들이 얼마나 서비스에 만족하는지를 질문하고 있기 때문에 설문 결과의 설명력이 떨어지고, 예보서비스의 전략수립에 활용되기 어렵다. 본 연구에서는 예보의 가치를 평가하는 유용한 도구인 $2{\times}2$ 검증테이블에 기존의 비용-손실이 아닌 만족-불만족 개념을 적용하여 상해와 서울에서 제공된 24시간 강수예보의 만족가치를 도출하였다. 그리고 예보에 대한 개인의 만족가치뿐만 아니라 예보 사용자 그룹의 만족가치를 평가하였다. 그 결과, 확정예보 사용자 그룹의 만족가치를 높이기 위해선 예보 정확도의 향상이 유용하지만, 확률예보의 경우엔 사용자 그룹의 불만족 정도와 예보확률의 사용 분포를 파악하는 것이 중요한 것으로 나타났다. 따라서 기상 커뮤니티들이 예보 사용자들에게 더 나은 만족가치를 제공할 수 있는 방안을 찾는데 도움을 줄 수 있을 것이다.

• 무역학회지
• /
• 제46권6호
• /
• pp.191-209
• /
• 2021

In this paper, various time series analysis models and machine learning models are presented for long-term prediction of export growth rate, and the prediction performance is compared and reviewed by RMSE and MAE. Export growth rate is one of the major economic indicators to evaluate the economic status. And It is also used to predict economic forecast. The export growth rate may have a negative (-) value as well as a positive (+) value. Therefore, Instead of using the ReLU function, which is often used for time series prediction of deep learning models, the PReLU function, which can have a negative (-) value as an output value, was used as the activation function of deep learning models. The time series prediction performance of each model for three types of data was compared and reviewed. The forecast data of long-term prediction of export growth rate was deduced by three forecast methods such as a fixed forecast method, a recursive forecast method and a rolling forecast method. As a result of the forecast, the traditional time series analysis model, ARDL, showed excellent performance, but as the time period of learning data increases, the performance of machine learning models including LSTM was relatively improved.

• The Journal of Asian Finance, Economics and Business
• /
• 제7권1호
• /
• pp.81-90
• /
• 2020

We examine the effects of the complexity of tax-related information on the issuance of analyst's pre-tax income forecast and its value relevance. If analysts respond adequately to the needs of investors, they are more likely to provide a pre-tax income forecast. The provision of a pre-tax income forecast may indicate analysts' confidence in assessing the quality of earnings. Thus, investors, in turn, would be more confident in the analysts' pre-tax income forecasts if analysts provide both pre-tax and earnings forecasts than only the latter. Using a sample of Korean listed companies for 2005-2014, we find that analysts are likely to provide an implicit tax forecast when the volatility of the effective tax rate is low and the book-tax differences are small. We also find that when analysts provide pre-tax and after tax income forecasts, the value relevance for unexpected earnings increases. These results indicate that analysts are likely to be interested in corporate tax information and the complexity of tax-related information affects the availability of implicit tax forecasts. Furthermore, this study provides empirical evidence that when analysts provide both pre-tax and after tax income forecasts, investors have more confidence in analysts' earnings forecasts, which results in greater investors' responses.

• 경영과학
• /
• 제26권1호
• /
• pp.77-91
• /
• 2009

It is the most important sucess factor for the electricity generation industry to minimize operations cost of surplus electricity generation through accurate demand forecasts. Temperature forecast is a significant input variable, because power demand is mainly linked to the air temperature. This study estimates the information value of the temperature forecast by analyzing the relationship between electricity load and daily air temperature in Korea. Firstly, several characteristics was analyzed by using a population-weighted temperature index, which was transformed from the daily data of the maximum, minimum and mean temperature for the year of 2005 to 2007. A neural network-based load forecaster was derived on the basis of the temperature index. The neural network then was used to evaluate the performance of load forecasts for various types of temperature forecasts (i.e., persistence forecast and perfect forecast) as well as the actual forecast provided by KMA(Korea Meteorological Administration). Finally, the result of the sensitivity analysis indicates that a $0.1^{\circ}C$ improvement in forecast accuracy is worth about $11 million per year.

• 한국항공운항학회지
• /
• 제15권3호
• /
• pp.57-62
• /
• 2007

In this paper, we attempted to produce the cloud forecast that use the numerical weather prediction(NWP) MM5 for objective cloud forecast. We presented two methods for cloud forecast. One of them used total cloud mixing ratio registered to sum(synthesis) of cloud-water and cloud-ice grain mixing ratio those are variables related to cloud among NWP result data and the other method that used relative humidity. An experiment was carried out period from 23th to 24th July 2004. According to the sequence of comparing the derived cloud forecast data with the observed value, it was indicated that both of those have a practical use possibility as cloud forecast method. Specially in this Case study, cloud forecast method that use total cloud mixing ratio indicated good forecast availability to forecast of the low level clouds as well as middle and high level clouds.

• The Journal of Asian Finance, Economics and Business
• /
• 제7권1호
• /
• pp.29-36
• /
• 2020

Stock price multiple is one of the most well-known equity valuation technique used to forecast equity price. It measures by multiplying "the ratio of stock price to a value driver" by a value driver. The value driver can be earning per share (EPS), sales or other financial measurements. The objective of price multiple technique is to evaluate the value of assets and compare how similar assets are priced in the market. Although stock price multiple technique is common in financial filed, studies on the application of the technique in Thailand is still limited. The present study is conducted to serve three major objectives. The first objective is to apply the technique to measure value of firms in banking sector in the Stock Exchange of Thailand. The second objective is to develop composite price multiple index to forecast equity prices. The third objective is to compare valuation accuracy of different value drivers of price multiple (i.e. EPS, Earnings Growth, Earnings Before Interest Taxes Depreciation and Amortization, Sales, Book Value and Composite Index) in forecasting equity prices. Results indicated that EPS is the most accurate value drivers of price multiple used to forecast equity price of firms in baking sector.

• 한국콘텐츠학회논문지
• /
• 제20권12호
• /
• pp.49-57
• /
• 2020

본 연구는 우리나라 수출 분야 산업의 경쟁력을 나타내는 부가가치율을 다양한 머신러닝 기법을 활용하여 예측하였다. 아울러, 예측의 정확성 및 안정성을 높이기 위하여 머신러닝 기법 예측값들에 예측조합 기법을 적용하였다. 특히, 본 연구는 산업별 부가가치율에 영향을 주는 다양한 변수를 고려하기 위하여 재귀적특성제거 방법을 사용하여 주요 변수를 선별한 후 머신러닝 기법에 적용함으로써 예측과정의 효율성을 높였다. 분석결과, 예측조합 방법에 따른 예측값은 머신러닝 기법 예측값들보다 실제의 산업 부가가치율에 근접한 것으로 나타났다. 또한, 머신러닝 기법의 예측값들이 큰 변동성을 보이는 것과 달리 예측조합 기법은 안정적인 예측값을 나타내었다.

• 천문학회보
• /
• 제41권1호
• /
• pp.80.1-80.1
• /
• 2016

There are probabilistic forecast models for solar flare occurrence, which can be evaluated by various skill scores (e.g. accuracy, critical success index, heidek skill score, and true skill score). Since these skill scores assume that two types of forecast errors (i.e. false alarm and miss) are equal or constant, which does not take into account different situations of users, they may be unrealistic. In this study, we make an evaluation of a probabilistic flare forecast model [Lee et al., 2012] which use sunspot groups and its area changes as a proxy of flux emergence. We calculate daily solar flare probabilities from 2011 to 2014 using this model. The skill scores are computed through contingency tables as a function of forecast probability, which corresponds to the maximum skill score depending on flare class and type of a skill score. We use a value score with cost/loss ratio, relative importance between the two types of forecast errors. The forecast probability (y) is linearly changed with the cost/loss ratio (x) in the form of y=ax+b: a=0.88; b=0 (C), a=1.2; b=-0.05(M), a=1.29; b=-0.02(X). We find that the forecast model has an effective range of cost/loss ratio for each class flare: 0.536-0.853(C), 0.147-0.334(M), and 0.023-0.072(X). We expect that this study would provide a guideline to determine the probability threshold and the cost/loss ratio for space weather forecast.