• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.2
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• pp.85-93
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• 2006

Since the SMP(System Marginal Price) is a vital factor to the market participants who intend to maximize the their profit and to the ISO(Independent System Operator) who wish to operate the electricity market in a stable sense, the short-term marginal price forecasting should be performed correctly. In an electricity market the short-term market price affects considerably the short-term trading between the market entities. Therefore, the exact forecasting of SMP can influence on the profit of market participants. This paper presents a new methodology for a day-ahead SMP forecasting using ARIMA(Autoregressive Integrated Moving Average) model based on the time-series method. And also the correction algorithm is proposed to minimize the forecasting error in order to improve the efficiency and accuracy of the SMP forecasting. To show the efficiency and effectiveness of the proposed method, the case studies are performed using historical data of SMP in 2004 published by KPX(Korea Power Exchange).

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.7
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• pp.405-413
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• 2004

Traditionally, electrical power systems had the vertically-integrated industry structures based on the economics of scale. However power systems have been recently reformed to increase the energy efficiency of the power system. According to these trends, Korean power industry has been partially restructured, and the competitive generation market was opened in 2001. In competitive electric markets, correct demand data are one of the most important issue to maintain the flexible electric markets as well as the reliable power systems. However, the measuring load data can have the uncertainty because of mechanical trouble, communication jamming, and other things. To obtain the reliable load data, an efficient evaluation technique to adust the missing load data is needed. This paper analyzes the load pattern of historical real data and then the turned ARIMA (Autoregressive Integrated Moving Average) model, PCHIP(Piecewise Cubic Interporation) and Branch & Bound method are applied to seek the missing parameters. The proposed method is tested under a variety of conditions and tested with historical measured data from the Korea Energy Management Corporation (KEMCO).

• Journal of the Korean Society for Aviation and Aeronautics
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• v.20 no.1
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• pp.66-75
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• 2012

The purpose of this study is to analyze the effect of intervention variables which may affect the air travel demand for Jeju domestic flights and to anticipate the air travel demand for Jeju domestic flights. The air travel demand forecasts for Jeju domestic flights are conducted through ARIMA-Intervention Model selecting five intervention variables such as 2002 World Cup games, SARS, novel swine-origin influenza A, Yeonpyeongdo bombardment and Japan big earthquake. The result revealed that the risk factor such as the threat of war that is a negative intervention incident and occurred in Korea has the negative impact on the air travel demand due to the response of risk aversion by users. However, when local natural disasters (earthquakes, etc) occurring in neighboring courtiers and global outbreak of an epidemic gave the negligible impact to Korea, negative intervention incident would have a positive impact on air travel demand as a response to find alternative due to rational expectation of air travel customers. Also we realize that a mega-event such as the 2002 Korea-Japan World Cup games reduced the air travel demand in a short-term period unlike the perception in which it will increase the air travel demand and travel demands in the corresponding area.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.1
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• pp.50-56
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• 2014

Accurate midterm load forecasting is essential to preventive maintenance programs and reliable demand supply programs. This paper describes a midterm load forecasting method using autoregressive integrated moving average (ARIMA) model which has been widely used in time series forecasting due to its accuracy and predictability. The various ARIMA models are examined in order to find the optimal model having minimum error of the midterm load forecasting. The proposed method is applied to forecast 104-week load pattern using the historical data in Korea. The effectiveness of the proposed method is evaluated by forecasting 104-week load from 2011 to 2012 by using historical data from 2002 to 2010.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.3
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• pp.323-331
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• 2021

With global warming and pollution problems, accurate forecasting of the harmful gases would be an essential alarm in our life. In this paper, we forecast the emission of the five gases(SOx, NO₂, NH₃, H₂S, CH₄) using the time series model of ARIMA, the learning algorithms of Random forest, and LSTM. We find that the gas emission data depends on the short-term memory and behaves like a random walk. As a result, we compare the RMSE, MAE, and MAPE as the measure of the prediction performance under the same conditions given to three models. We find that ARIMA forecasts the gas emissions more precisely than the other two learning-based methods. Besides, the ARIMA model is more suitable for the real-time forecasts of gas emissions because it is faster for modeling than the two learning algorithms.

• The Korean Journal of Applied Statistics
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• v.36 no.5
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• pp.457-471
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• 2023

The monthly series is an aggregation of daily values. In the absence of observable daily data, calendar effects such as trading day and holidays are estimated using a RegARIMA model. However, if the daily series were observable, these calendar effects could be estimated directly from the daily series, potentially improving the seasonal adjustment of the monthly time series. In this paper, we propose a method to improve the seasonal adjustment of monthly time series by using calendar variation estimation based on daily time series. We apply this seasonal adjustment method to three monthly time series and compare our results with those obtained using X-13ARIMA-SEATS.

• Annual Conference on Human and Language Technology
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• 2003.10d
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• pp.81-85
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• 2003

이 논문의 목적은 코퍼스 크기에 따른 타입과 토큰간의 관계를 엄밀한 통계적 방법으로 그 특징을 밝히고자 하는 것이다. 지금까지 코퍼스를 구축하는 데 있어서, 자료의 다양성을 고려한 자료 균형성을 문제와 더불어 코퍼스 구축 규모의 문제는 매우 중요한 고려사항이었다. 이런 문제는 일찍이 영어 코퍼스를 중심으로 많은 연구가 진행된 바가 있지만 한국어를 대상으로 한 엄밀한 연구는 많이 이루어지지 않았다. 이 연구에서는 현재까지 구축한 현대 한국어 말뭉치 1억여 어절을 대상으로 말뭉치 크기 증가에 따른 타입과 토큰간의 통계적 관계를 3가지 모형에 대해 비교하였으며 최종적으로 ARIMA모형을 이용하여 그 함수적 관계를 밝혀보았다. 연구 결과에 따르면 한국어 자료는 약 1천만 어절의 토큰을 기준으로 타입의 변화가 다소 둔화되는 결과를 보인다. 연구에 의해 도출된 함수식을 이용하면 소규모의 자료를 이용하더라도, 대규모 자료에서의 타입수를 계산해 낼 수 있으므로, 더욱 다양하고 정확한 통계처리의 근거를 제시할 수 있게 된다.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.257-267
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• 2000

The purpose of this study is to analyse the economic effects of General Elections in Korea, in other words, to test the Political Business Cycle hypothesis using ARIMA-Intervention Analysis.

• Proceedings of the Safety Management and Science Conference
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• 2008.04a
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• pp.461-468
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• 2008

<그림 1>과 <그림 2>, <그림 3>은 통계 패키지(Econometric Views)를 사용하여 제조업용 로봇의 수출입을 2007년부터 2008년까지 추정한 값이며, 자료는 2001년도 이후의 관세청 수출입 실적자료를 활용하였다. <그림 1>은 SAENGF는 제조업용 로봇 국내생산의 추정치이며, 점선은 95% 신뢰구간을 의미한다. <그림 2>는 통계 패키지(Econometric Views)를 사용하여 제조업용 로봇의 수출을 2007년부터 2008년까지 추정한 값이며, EXPORTF는 제조업용 로봇 수출의 추정치이며, 점선은 95% 신뢰구간을 의미한다. <그림 3>은 통계 패키지(Econometric Views)를 사용하여 제조업용 로봇의 수입을 2007년부터 2008년까지 추정한 값이며, IMPORTF는 제조업용 로봇 수출의 추정치이며, 점선은 95% 신뢰구간을 의미한다. <표 1>은 국내 제조업용 로봇의 국내생산, 수출과 수입의 추정치이며, ARIMA모형을 사용하였으며, 자료는 2001년도 이후의 데이터로 관세청 수출입 실적자료를 활용하였다.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.63-76
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• 2006

Marine production forecasting in fisheries is a crucial factor for managing and maintaining fishery resources. Thus this paper aims to generate a forecasting model of total marine production. The most generally method of time series model is to generate the most optimal single forecasting model. But the method could induce a different forecasting results when it does not properly infer a model To overcome the defect, I am trying to propose a single forecasting through multiple time series model. In other word, by comparing and integrating the output resulted from ARIMA and VAR model (which are typical method in a forecasting methodology), I tried to draw a forecasting. It is expected to produce more stable and delicate forecasting prospect than a single model. Through this, I generated 3 models on a yearly and monthly data basis and then here I present a forecasting from 2006 to 2010 through comparing and integrating 3 models. In conclusion, marine production is expected to show a decreasing tendency for the coming years.