• 산경연구논집
• /
• 제9권10호
• /
• pp.83-89
• /
• 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.

• 정보처리학회논문지:소프트웨어 및 데이터공학
• /
• 제11권2호
• /
• pp.81-86
• /
• 2022

시계열 데이터는 주식, IoT, 공장 자동화와 같은 다양한 실생활에서 수집되고 활용되고 있으며, 정확한 시계열 예측은 해당 분야에서 운영 효율성을 높일 수 있어서 전통적으로 중요한 연구 주제이다. 전반적인 시계열 데이터의 향상된 특징을 추출할 수 있는 대표적인 시계열 데이터 분석 방법인 다층 수평 예측은 최근 부가적 정보를 포함하는 시계열 데이터에 내재한 이질성(heterogeneity)까지 포괄적으로 분석에 활용하여 향상된 시계열 예측한다. 하지만 대부분의 심층 학습 기반 시계열 분석 모델들은 시계열 데이터의 이질성을 반영하지 못했다. 따라서 우리는 잘 알려진 temporal fusion transformers 방법을 사용하여 실생활과 밀접한 실제 데이터를 이질성을 고려한 다층 수평 예측에 적용하였다. 결과적으로 주식, 미세먼지, 전기 소비량과 같은 실생활 시계열 데이터에 적용한 방법이 기존 예측 모델보다 향상된 정확도를 가짐을 확인할 수 있었다.

• 무역학회지
• /
• 제47권5호
• /
• pp.21-37
• /
• 2022

Baltic Dry Index (BDI) is difficult to forecast because of the high volatility and complexity. To improve the BDI forecasting ability, this study apply Bayesian variable selection method with a large number of predictors. Our estimation results based on the BDI and all predictors from January 2000 to September 2021 indicate that the out-of-sample prediction ability of the ADL model with the variable selection is superior to that of the AR model in terms of point and density forecasting. We also find that critical predictors for the BDI change over forecasts horizon. The lagged BDI are being selected as an key predictor at all forecasts horizon, but commodity price, the clarksea index, and interest rates have additional information to predict BDI at mid-term horizon. This implies that time variations of predictors should be considered to predict the BDI.

• 국제학술발표논문집
• /
• The 6th International Conference on Construction Engineering and Project Management
• /
• pp.174-178
• /
• 2015

This paper presents a new method for forecasting construction project cost and time at completion or at any intermediate time horizon of the project duration. The method is designed to overcome identified limitations of current applications of earned value method in forecasting project cost and time. The proposed method usesfuzzy set theory to model uncertainties associated with project performance and it integrates the earned value technique and the contractors' judgement. The fuzzy set theory is applied as an alternative approach to deterministic and probabilistic methods. Using fuzzy set theory allows contractors to: (1) perform risk analysis for different scenarios of project performance indices, and (2) perform different scenarios expressing vagueness and imprecision of forecasted project cost and time using a set of measures and indices. Unlike the current applications of Earned Value Method(EVM), The proposed method has a numberof interesting features: (1) integrating contractors' judgement in forecasting project performance; (2) enabling contractors to evaluate the risk associated with cost overrun in much simpler method comparing with that of simulation, and (3) accounting for uncertainties involved in the forecasting project cost.

• 한국농공학회:학술대회논문집
• /
• 한국농공학회 2002년도 학술발표회 발표논문집
• /
• pp.277-280
• /
• 2002

An artificial neural network model was developed to analyze and forecast real time river runoff from the Naju watershed, in Korea. Model forecasts are very accurate (i.e., relative error is less than 3% and $R^2$ is great than 0.99) for calibration data sets. Increasing the time horizon for validation data sets, thus making the model suitable for flood forecasting, decreases the accuracy of the model. The resulting optimal EBPN models for forecasting real time runoff consists of ten rainfall and four and ten runoff data (ANN0410 and ANN1010 models). Performances of the ANN0410 and ANN1010 models remain satisfactory up to 6 hours (i.e., $R^2$ is great than 0.92).

• 한국농공학회지
• /
• 제45권2호
• /
• pp.45-57
• /
• 2003

An artificial neural network model was developed to analyze and forecast Short-term river runoff from the Naju watershed, in Korea. Error back propagation neural networks (EBPN) of hourly rainfall and runoff data were found to have a high performance In forecasting runoff. The number of hidden nodes were optimized using total error and Bayesian information criterion. Model forecasts are very accurate (i.e., relative error is less than 3% and $R^2$is greater than 0.99) for calibration and verification data sets. Increasing the time horizon for application data sets, thus mating the model suitable for flood forecasting. decreases the accuracy of the model. The resulting optimal EBPN models for forecasting hourly runoff consists of ten rainfall and four runoff data(ANN0410 model) and ten rainfall and ten runoff data(ANN1010 model). Performances of the ANN0410 and ANN1010 models remain satisfactory up to 6 hours (i.e., $R^2$is greater than 0.92).

• 한국경영과학회:학술대회논문집
• /
• 대한산업공학회/한국경영과학회 1996년도 춘계공동학술대회논문집; 공군사관학교, 청주; 26-27 Apr. 1996
• /
• pp.505-510
• /
• 1996

Many diseases cause other diseases with strength of influences and time intervals. Prognostic and therapeutic assessments are the important part of clinical medicine as well as diagnostic assessments. In cases where a patient already has manufestations of multiple disorders (complications), progress forecasting and therapy decision by physicians without support tools are very dificult: physicians often say that "Once complications set in, the patient may die". Treating complications are difficult tasks for physicians, because they have to consider all of the complexities, possibilities and interactions between the diseases. The prediction of multiple disorders has many bundles that arise from such time-dependent interrelationships between diseases and nonlinear progress. This paper proposes a model based on time-dependent influences, which appropriately describes the progress of mulitple disorders, and gives some modificaitons for applying this model to medical domains: time-dependent influence matrix manifestation vector, therapy efficacy matrix, S-shaped curve approximation, definitions of which are provided. This research proposes an algorithm for forecasting the state of each disease on the time horizon and for evaluation of therapy alternatives with not toy example, but real patient history of multiple disorders.disorders.

• 한국산림과학회지
• /
• 제84권2호
• /
• pp.121-130
• /
• 1995

미국 미시간주의 여행수요(旅行需要)를 예측(豫測)하기 위하여 사용되어진 여러 모델들의 예측정확성(豫測正確性)이 검토되었다. 8가지의 연년(連年)모델들은 2년까지 예측하는데 그리고 9가지의 분기(分期)모델들은 4분기(分期)까지 예측하는데 사용되어 졌다. 연년(連年)모델의 예측정확성(豫測正確性) 평가(評價)에서, 중회귀(重回歸)모델은 1년과 2년을 예측(豫測)하는데 있어 다른 방법들 보다 더 정확(正確)했다. 분기(分期)모델에 있어서는, Winters' exponential smoothing와 Box-Jenkins 방법이 1 분기예측(分期豫測)에 있어 naive 1 s 보다 더 정확(正確)했으나 2분기(分期), 3분기(分期), 4분기(分期)를 예측(豫測)하는데 이 방법(方法)들은 naive 1 s 보다 정확(正確)하지 않았다. 정교(精巧)한 모델들은 분기별(分期別) 예측(豫測)을 하는데 있어서 단순(單純)한 모델들보다 더 정확(正確)하지 않았다. 연년(連年)모델과 분기(分期)모델을 이용한 1년간(年間) 예측비교(豫測比較)에서, 중회귀모형(重回歸模型)은 연간자료(年間資料)보다 분기자료(分期資料)에 적용(適用)할 때 더 좋은 결과(結果)를 얻었으나 그 차이(差異)가 미약(微弱)하며 다른 모델들은 일관성(一貫性)있게 좋은 결과(結果)를 갖지 않으므로 연년(連年)모델보다 分期모델을 사용하도록 강력하게 권장할 수 없다. 연년(連年)모델은 기대(期待)하였던 것처럼 예측기간(豫測期間)이 길어짐으로서 예측정확성(豫測正確性)이 감소(減少)하였으나 분기(分期)모델은 이같은 결과(結果)를 나타내지 않았다.

• 한국통신학회논문지
• /
• 제42권4호
• /
• pp.932-938
• /
• 2017

본 논문에서는 전력망 연계형 마이크로그리드의 분산에너지자원을 위한 에너지관리시스템의 구조에 대해 서술한다. 전력망 연계형 마이크로그리드의 분산에너지자원 에너지관리시스템은 분산에너지자원들의 상태나 시간대별 차등요금제와 같은 마이크로그리드 내외의 각종 정보들을 다양한 프로토콜들을 통해 수집한다. 에너지관리시스템은 수집한 정보들을 바탕으로 예측과 최적화 계산을 수행하고, 전기요금 절감이라는 마이크로그리드 최적운영 목표를 달성할 수 있도록 분산에너지자원들의 운전 스케줄을 도출한다. 최적운영 달성을 위하여 에너지관리시스템은 내부적으로 효과적 스케줄 도출을 위한 알고리즘을 포함하고 있어야하며, 도출한 스케줄을 외부의 분산에너지자원에 전달할 수 있는 프로토콜을 갖추어야 한다. 예측 과정에서 발생하는 실제와의 오차를 줄이기 위하여 에너지관리시스템은 rolling horizon controller로 작동한다. 도출된 운전 스케줄은 국제표준프로토콜을 통하여 실시간으로 분산에너지자원에 전달되어 마이크로그리드 최적운영을 가능하도록 한다.

• Journal of Electrical Engineering and Technology
• /
• 제13권6호
• /
• pp.2227-2235
• /
• 2018

Under the current policies and compensation rules in South Korea, Photovoltaic (PV) generation supplier can maximize the profit by combining PV generation with Energy Storage System (ESS). However, the existing operational strategy of ESS is not able to maximize the profit due to the limitation of ESS capacity. In this paper, new ESS scheduling algorithm is introduced by utilizing the System Marginal Price (SMP) and PV generation forecasting to maximize the profits of PV generation supplier. The proposed algorithm determines the charging time of ESS by ranking the charging schedule from low to high SMP when PV generation is more than enough to charge ESS. The discharging time of ESS is determined by ranking the discharging schedule from high to low SMP when ESS energy is not enough to maintain the discharging. To compensate forecasting error, the algorithm is updated every hour to apply the up-to-date information. The simulation is performed to verify the effectiveness of the proposed algorithm by using actual PV generation and ESS information.