• Title/Summary/Keyword: forecasting models

Search Result 1,009, Processing Time 0.028 seconds

Korean Ocean Forecasting System: Present and Future (한국의 해양예측, 오늘과 내일)

  • Kim, Young Ho;Choi, Byoung-Ju;Lee, Jun-Soo;Byun, Do-Seong;Kang, Kiryong;Kim, Young-Gyu;Cho, Yang-Ki
    • The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
    • /
    • v.18 no.2
    • /
    • pp.89-103
    • /
    • 2013
  • National demands for the ocean forecasting system have been increased to support economic activity and national safety including search and rescue, maritime defense, fisheries, port management, leisure activities and marine transportation. Further, the ocean forecasting has been regarded as one of the key components to improve the weather and climate forecasting. Due to the national demands as well as improvement of the technology, the ocean forecasting systems have been established among advanced countries since late 1990. Global Ocean Data Assimilation Experiment (GODAE) significantly contributed to the achievement and world-wide spreading of ocean forecasting systems. Four stages of GODAE were summarized. Goal, vision, development history and research on ocean forecasting system of the advanced countries such as USA, France, UK, Italy, Norway, Australia, Japan, China, who operationally use the systems, were examined and compared. Strategies of the successfully established ocean forecasting systems can be summarized as follows: First, concentration of the national ability is required to establish successful operational ocean forecasting system. Second, newly developed technologies were shared with other countries and they achieved mutual and cooperative development through the international program. Third, each participating organization has devoted to its own task according to its role. In Korean society, demands on the ocean forecasting system have been also extended. Present status on development of the ocean forecasting system and long-term plan of KMA (Korea Meteorological Administration), KHOA (Korea Hydrographic and Oceanographic Administration), NFRDI (National Fisheries Research & Development Institute), ADD (Agency for Defense Development) were surveyed. From the history of the pre-established systems in other countries, the cooperation among the relevant Korean organizations is essential to establish the accurate and successful ocean forecasting system, and they can form a consortium. Through the cooperation, we can (1) set up high-quality ocean forecasting models and systems, (2) efficiently invest and distribute financial resources without duplicate investment, (3) overcome lack of manpower for the development. At present stage, it is strongly requested to concentrate national resources on developing a large-scale operational Korea Ocean Forecasting System which can produce open boundary and initial conditions for local ocean and climate forecasting models. Once the system is established, each organization can modify the system for its own specialized purpose. In addition, we can contribute to the international ocean prediction community.

통신 서비스 확산모형

  • Sin, Chang-Hun;Park, Seok-Ji
    • ETRI Journal
    • /
    • v.10 no.1
    • /
    • pp.39-52
    • /
    • 1988
  • This study suggests the diffusion models to predict the spread pattern of telecommunications services. The extended models containing both (either) price and (or) income varible are offered on the basis of Bass model. At the empirical test using Korean telephone data, the models with either price or income varible are the best forecasting model under apriori selected econometric criteria.

  • PDF

전국 장래 승용차 보유대수 추정에 관한 연구

  • 원제무;홍성표;유정복
    • Journal of Korean Society of Transportation
    • /
    • v.7 no.1
    • /
    • pp.5-18
    • /
    • 1989
  • Understanding the future level of car ownership is essential in order to formulate various transportation policies. Despite its importance, however, a revies of literature indicates that previous studies treated car ownership as a linear function of income, GNP, degree of urbanization and etc. more detaild and accurate models of car ownership should be possible if information on major variables determining car ownership can be gathered. In this paper, three approaches have been chosen to develop mathematical models to predict future increase in car ownership in Korea. The three methords developed are : income distribution methords ; multiple regression models ; forecasting curves.

  • PDF

COMPARATIVE ANALYSIS ON MACHINE LEARNING MODELS FOR PREDICTING KOSPI200 INDEX RETURNS

  • Gu, Bonsang;Song, Joonhyuk
    • The Pure and Applied Mathematics
    • /
    • v.24 no.4
    • /
    • pp.211-226
    • /
    • 2017
  • In this paper, machine learning models employed in various fields are discussed and applied to KOSPI200 stock index return forecasting. The results of hyperparameter analysis of the machine learning models are also reported and practical methods for each model are presented. As a result of the analysis, Support Vector Machine and Artificial Neural Network showed a better performance than k-Nearest Neighbor and Random Forest.

Application of Artificial Neural Network Ensemble Model Considering Long-term Climate Variability: Case Study of Dam Inflow Forecasting in Han-River Basin (장기 기후 변동성을 고려한 인공신경망 앙상블 모형 적용: 한강 유역 댐 유입량 예측을 중심으로)

  • Kim, Taereem;Joo, Kyungwon;Cho, Wanhee;Heo, Jun-Haeng
    • Journal of Wetlands Research
    • /
    • v.21 no.spc
    • /
    • pp.61-68
    • /
    • 2019
  • Recently, climate indices represented by quantifying atmospheric-ocean circulation patterns have been widely used to predict hydrologic variables for considering long-term climate variability. Hydrologic forecasting models based on artificial neural networks have been developed to provide accurate and stable forecasting performance. Forecasts of hydrologic variables considering climate variability can be effectively used for long-term management of water resources and environmental preservation. Therefore, identifying significant indicators for hydrologic variables and applying forecasting models still remains as a challenge. In this study, we selected representative climate indices that have significant relationships with dam inflow time series in the Han-River basin, South Korea for applying the dam inflow forecasting model. For this purpose, the ensemble empirical mode decomposition(EEMD) method was used to identify a significance between dam inflow and climate indices and an artificial neural network(ANN) ensemble model was applied to overcome the limitation of a single ANN model. As a result, the forecasting performances showed that the mean correlation coefficient of the five dams in the training period is 0.88, and the test period is 0.68. It can be expected to come out various applications using the relationship between hydrologic variables and climate variability in South Korea.

An introduction of new time series forecasting model for oil cargo volume (유류화물 항만물동량 예측모형 개발 연구)

  • Kim, Jung-Eun;Oh, Jin-Ho;Woo, Su-Han
    • Journal of Korea Port Economic Association
    • /
    • v.34 no.1
    • /
    • pp.81-98
    • /
    • 2018
  • Port logistics is essential for Korea's economy which heavily rely on international trade. Vast amounts of capital and time are consumed for the operation and development of ports to improve their competitiveness. Therefore, it is important to forecast cargo volume in order to establish the optimum level of construction and development plan. Itemized forecasting is necessary for appropriate port planning, since disaggregate approach is able to provides more realistic solution than aggregate forecasting. We introduce a new time series model which is Two-way Seasonality Multiplied Regressive Model (TSMR) to forecast oil cargo volume, which accounts for a large portion of total cargo volume in Korea. The TSMR model is designed to take into account the characteristics of oil cargo volume which exhibits trends with short and long-term seasonality. To verify the TSMR model, existing forecasting models are also used for a comparison reason. The results shows that the TSMR excels the existing models in terms of forecasting accuracy whereas the TSMR displays weakness in short-term forecasting. In addition, it was shown that the TSMR can be applied to other cargoes that have trends with short- and long-term seasonality through testing applicability of the TSMR.