• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.338-341
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• 2003

Recently, the chaotic method is employed to forecast a near future of uncertain phenomena. This method makes it possible by restructuring an attractor of given time-series data in multi-dimensional space through Takens' embedding theory. However, many economical time-series data are not sufficiently chaotic. In other words, it is hard to forecast the future trend of such economical data on the basis of chaotic theory. In this paper, time-series data are divided into wave components using wavelet transform. It is shown that some divided components of time-series data show much more chaotic in the sense of correlation dimension than the original time-series data. The highly chaotic nature of the divided component enables us to precisely forecast the value or the movement of the time-series data in near future. The up and down movement of TOPICS value is shown so highly predicted by this method as 70％.

• The Journal of Industrial Distribution & Business
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• v.9 no.10
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• pp.83-89
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• 2018

Purpose - The purpose of this study examines when the optimism impact on financial asset price forecasting and the boundary condition of optimism in the financial asset price forecasting. People generally tend to optimistically forecast their future. Optimism is a nature of human beings and optimistic forecasting observed in daily life. But is it always observed in financial asset price forecasting? In this study, two factors were focused on considering whether the optimism that people have applied to predicting future performance of financial investment products (e.g., mutual fund). First, this study examined whether the degree of optimism varied depending on the direction of the prior price trend. Second, this study examined whether the degree of optimism varied according to the forecast period by dividing the future forecasted by people into three time horizon based on forecast period. Research design, data, and methodology - 2 (prior price trend: rising-up trend vs falling-down trend) × 3 (forecast time horizon: short term vs medium term vs long term) experimental design was used. Prior price trend was used between subject and forecast time horizon was used within subject design. 169 undergraduate students participated in the experiment. χ² analysis was used. In this study, prior price trend divided into two types: rising-up trend versus falling-down trend. Forecast time horizon divided into three types: short term (after one month), medium term (after one year), and long term (after five years). Results - Optimistic price forecasting and boundary condition was found. Participants who were exposed to falling-down trend did not make optimistic predictions in the short term, but over time they tended to be more optimistic about the future in the medium term and long term. However, participants who were exposed to rising-up trend were over-optimistic in the short term, but over time, less optimistic in the medium and long term. Optimistic price forecasting was found when participants forecasted in the long term. Exposure to prior price trends (rising-up trend vs falling-down trend) was a boundary condition of optimistic price forecasting. Conclusions - The results indicated that individuals were more likely to be impacted by prior price tends in the short term time horizon, while being optimistic in the long term time horizon.

• Korean Management Science Review
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• v.28 no.3
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• pp.143-159
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• 2011

Recently, the level of uncertainty in R&D investment for an enterprise has increased due to technological development and industrial changes. Accordingly it is necessary for an enterprise to forecast the future or foresight the future technologies. But, the fact that the methodology used in predicting future technology is suitable for large project makes enterprise difficult to forecast the future technologies or trends. Thus, this study seeks for available methodology for future technology foresight from enterprise standpoint. The methodology proposed in this research is based on the scenario model, especially focused on the customer needs and future society change.

• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.67-80
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• 2017

Over the last few decades, ensemble forecasts based on global climate models have become an important part of climate forecast due to the ability to reduce uncertainty in prediction. Moreover in ensemble forecast, assessing the prediction uncertainty is as important as estimating the optimal weights, and this is achieved through a probabilistic forecast which is based on the predictive distribution of future climate. The Bayesian model averaging has received much attention as a tool of probabilistic forecasting due to its simplicity and superior prediction. In this paper, we propose a new Bayesian model averaging method for probabilistic ensemble forecasting. The proposed method combines a deterministic ensemble forecast based on a multivariate regression approach with Bayesian model averaging. We demonstrate that the proposed method is better in prediction than the standard Bayesian model averaging approach by analyzing monthly average precipitations and temperatures for ten cities in Korea.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1689-1690
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• 2008

In this paper, The method of a temperature rasing forecast is suggested and simulated. The data used in this simulations exists in the KD Power and it was obtain by real transformer. The method of temperature forecast is based on a top oil temperature rising modeling which is proposed by the IEEE journal. We propose modifications of a modeling that accurately predicts a future transformer temperature. This Method is verified by simulations.

• Journal of Distribution Science
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• v.20 no.12
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• pp.117-123
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• 2022

Purpose: This study investigates the relationship between earnings forecasts estimated from a cross-sectional earnings forecast model and firm characteristics such as firm size, sales volatility, and earnings volatility. Research design, data and methodology: The association between earnings forecasts and the aforementioned firm characteristics is examined using 214 firm-year observations with analyst following and 848 firm-year observations without analyst following for the period of 2011-2019. I estimate future earnings using a cross-sectional earnings forecast model, and then compare these model-based earnings forecasts with analysts' earnings forecasts in terms of forecast bias and forecast accuracy. The earnings forecast bias and accuracy are regressed on firm size, sales volatility, and earnings volatility. Results: For a sample with analyst following, I find that the model-based earnings forecasts are more accurate as the firm size is larger, whereas the analysts' earnings forecasts are less biased and more accurate as the firm size is larger. However, for a sample without analyst following, I find that the model-based earnings forecasts are more pessimistic and less accurate as firms' past earnings are more volatile. Conclusions: Although model-based earnings forecasts are useful for evaluating firms without analyst following, their accuracy depends on the firms' earnings volatility.

• Atmosphere
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• v.14 no.3
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• pp.3-18
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• 2004

A heavy snowfall event occurred over the middle part of the Korean peninsula on March 4, 2004. The numerical models of KMA failed to forecast this heavy snowfall event because this event was due to small scale disturbance by low lever convergence and atmospheric instability. The analyses for this heavy snowfall have been performed to give forecasters useful suggestions for forecasting heavy snowfall events in the future. The analyses for the snowfall event were recounted by the Hourly Korean Peninsula Analysis Weather Chart (HKPAWC) presenting on the KMA intranet system. We confirmed that warm air flows of low level into south central Korea in conjunction with strong southwesterly winds played important role in the heavy snowfall event. We suggested several check points to improve the forecast of heavy snowfall events in the future through the results of the analyses.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.147-147
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• 2019

The Numerical Weather Prediction (NWP) models determine the future state of the weather by forcing current weather conditions into the atmospheric models. The NWP models approximate mathematically the physical dynamics by nonlinear differential equations; however these approximations include uncertainties. The errors of the NWP estimations can be related to the initial and boundary conditions and model parameterization. Development in the meteorological forecast models did not solve the issues related to the inevitable biases. In spite of the efforts to incorporate all sources of uncertainty into the forecast, and regardless of the methodologies applied to generate the forecast ensembles, they are still subject to errors and systematic biases. The statistical post-processing increases the accuracy of the forecast data by decreasing the errors. Error prediction of the NWP models which is updating the NWP model outputs or model output statistics is one of the ways to improve the model forecast. The regression methods (including linear, polynomial and scaling regression) are applied to the present study to improve the real time forecast skill. Such post-processing consists of two main steps. Firstly, regression is built between forecast and measurement, available during a certain training period, and secondly, the regression is applied to new forecasts. In this study, the WRF real-time forecast data, in comparison with the observed data, had systematic biases; the errors related to the NWP model forecasts were reflected in the underestimation of the meteorological data forecast by the WRF model. The promising results will indicate that the post-processing techniques applied in this study improved the meteorological forecast data provided by WRF model. A comparison between various bias correction methods will show the strength and weakness of the each methods.

• Journal of Korean Library and Information Science Society
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• v.25
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• pp.19-50
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• 1996

The purpose of this paper is to study on ownership and access in future library. For this purpose, this is criticized about recognition regarding future library of Library and Information Science researchers in Korea. And, this is reviewed the present stages of collection development and a role of future books, future libraries and future librarians in Korea. The result of this study is known unrealistic reality analysis and forecast surrounding future library discourse and at the same time that following Western model is not fit for future library in Korea. This study is proposed resolving of problems to access based on physical collection in future library.

• Journal of Korean Library and Information Science Society
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• v.25
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• pp.1-18
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• 1996

The purpose of this paper is to study on ownership and access in future library. For this purpose, this is criticized about recognition regarding future library of Library and Information Science researchers in Korea. And this is reviewed the present stages of collection development and a role of future books, future libraries and future librarians in Korea. The result of this study is known unrealistic reality analysis and forecast surrounding future library discourse and at the same time that following Western model is not fit for future library in Korea. This study is proposed resolving of problems to access based on physical collection in future library.