• Communications for Statistical Applications and Methods
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• 제16권4호
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• pp.723-730
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• 2009

시계열의 결측값은 미지의 모수로 취급될 수 있으며 최대우도방법 또는 확률변수방법에 의해 추정할 수 있으며 또한 주어진 자료 하에서 미지의 값에 대한 조건부기대치로 예측할 수 있다. 이 연구의 주된 목적은 불완전한 자료에 대해 ARMA 모형을 적용하여 두 가지 추정방법인 최대우도추정방법과 확률변수방법을 이용해 결측값을 대체하는 방법을 비교하는데 있다. 사례분석을 위해 한국질병관리본부에서 전산보고 하고 있는 전염병 자료 중에서 2001${\sim}$2006년 동안의 월별 Mumps 자료를 이용하여 앞의 두 가지 추정방법을 예측오차제곱합(SSF)을 구하여 비교한다.

• 품질경영학회지
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• 제17권2호
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• pp.17-26
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• 1989

This paper is concerned with forecasting the existing number of errors in the computer software and optimizing the stopping time of the software test based upon the forecasted number of errors. The most commonly used models have assessed software reliability under the assumption that the software failure late is proportional to the current fault content of the software but invariant to time since software faults are independents of others and equally likely to cause a failure during testing. In practice, it has been observed that in many situations, the failure rate decrease. Hence, this paper proposes a mathematical model to describe testing situations where the failure rate of software limearly decreases proportional to testing time. The least square method is used to estimate parameters of the mathematical model. A cost model to optimize the software testing time is also proposed. In this cost mode two cost factors are considered. The first cost is to test execution cost directly proportional to test time and the second cost is the failure cost incurred after delivery of the software to user. The failure cost is assumed to be proportional to the number of errors remained in the software at the test stopping time. The optimal stopping time is determined to minimize the total cost, which is the sum of test execution cast and the failure cost. A numerical example is solved to illustrate the proposed procedure.

• 스마트미디어저널
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• 제12권11호
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• pp.134-144
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• 2023

Nowadays, Deep Learning (DL) technology is being used in several government departments. South Korea imports a lot of seafood. If the demand for fishery products is not accurately predicted, then there will be a shortage of fishery products and the price of the fishery product may rise sharply. So, South Korea's Ministry of Ocean and Fisheries is attempting to accurately predict seafood imports using deep learning. This paper introduces the solution for the fish import prediction in South Korea using the Long Short-Term Memory (LSTM) method. It was found that there was a huge gap between the sum of consumption and export against the sum of production especially in the case of two species that are Hairtail and Pollock. An import prediction is suggested in this research to fill the gap with some advanced Deep Learning methods. This research focuses on import prediction using Machine Learning (ML) and Deep Learning methods to predict the import amount more precisely. For the prediction, two Deep Learning methods were chosen which are Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). Moreover, the Machine Learning method was also selected for the comparison between the DL and ML. Root Mean Square Error (RMSE) was selected for the error measurement which shows the difference between the predicted and actual values. The results obtained were compared with the average RMSE scores and in terms of percentage. It was found that the LSTM has the lowest RMSE score which showed the prediction with higher accuracy. Meanwhile, ML's RMSE score was higher which shows lower accuracy in prediction. Moreover, Google Trend Search data was used as a new feature to find its impact on prediction outcomes. It was found that it had a positive impact on results as the RMSE values were lowered, increasing the accuracy of the prediction.

• 응용통계연구
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• 제20권1호
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• pp.91-102
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• 2007

본 논문은 공간시계열 자기회귀 이동평균(STARMA) 모형과 공간 시계열 중선형(STBL) 모형에 대해 식별, 추정, 예측 등의 통계적 절차와 특징들을 논하고, 두 모형을 비교하는데 목적이 있다. 사례 연구를 위 해 2001년부터 2006년까지 8개 지역으로부터 보고된 월별 Mumps 자료를 사용했고, 예측오차제곱합(SSF)을 활용하여 두 모형의 적합도를 비교하였다.