• 대기
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• 제23권3호
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• pp.265-273
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• 2013

To investigate properties of cloud and rainfall occurred at Boseong on 10 July 2012, Raindrop Size distributions (RSDs) and other parameters were analyzed using observation data collected by Micro Rain Radar (MRR) and PARticle SIze and VELocity (PARSIVEL) disdrometer located in the National center for intensive observation of severe weather at Boseong in the southwest of the Korean peninsula. In addition, time series of RSD parameters, relationship between reflectivity-rain rate, and vertical variation of rain rates-fall velocities below melting layer were examined. As a result, good agreements were found in the reflectivity-rain rate time series as well as their power relationships between MRR and PARSIVEL disdrometer. The rain rate was proportional to reflectivity, mean diameter, and inversely proportional to shape (${\mu}$), slope (${\Lambda}$), intercept ($N_0$) parameter of RSD. In comparison of the RSD, as rain rate was increased, the slope of RSD became less steep and the mean diameter became larger. Also, it was verified that reflectivities are classified in three categories (Category 1: Z (reflectivity) > 40 dBZ, Category 2: 30 dBZ < Z < 40 dBZ, Category 3: Z < 30 dBZ). As reflectivity was increased, rain rate was intensified and larger raindrops were existed, while reflectivity was decreased, shape (${\mu}$), slope (${\Lambda}$), intercept ($N_0$) parameter of RSD were increased. We expected that these results will lead to better understanding of microphysical process in convective rainfall system occurred during short-term period over Korean peninsula.

• 한국지능시스템학회논문지
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• 제16권2호
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• pp.172-178
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• 2006

본 논문에서는 시계열 예측을 이용한 법원경매 정보제공 시스템을 개발하였다. 이 시스템은 권리분석을 위하여 낙찰가를 예측하고, 낙찰예측가에 따라 배당 정보를 제공하도록 설계되어 있으며, 이를 구현하기 위하여 물건 자료의 입력 인터페이스와 정보 제공을 위한 웹 인터페이스를 구축하였다. 자료 입력 인터페이스는 자료의 입력, 수정, 삭제의 기능을 가지며, 웹 인터페이스는 법원경매 물건을 중심으로 관련 정보를 제공한다. 실시간 정보 제공에 초점을 두고 자동 권리분석이 가능하도록 하기 위하여 낙찰가를 시계열 자료로 표현하여 낙찰예상가를 예측 방법을 제안하고, 기존의 방법과 비교 실험을 통하여 제안방법의 유효성을 검증한다.

• 한국수자원학회논문집
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• 제46권12호
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• pp.1249-1263
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• 2013

장기간의 가뭄에 의한 피해를 최소화하기 위해서는 유역에 적합한 가뭄관리 대책의 수립과 함께 미래에 발생하게 될 가뭄을 미리 예측할 수 있는 기술이 구축되어야 한다. 또한 미래의 가뭄에 대한 합리적 대응 방안을 수립하기 위해서는 가뭄의 지속기간(duration)과 심도(severity)의 정량적인 예측이 선행되어야 한다. 본 연구에서는 수문 시계열의 예측에 가장 많이 이용되고 있는 대표적인 통계학적 기법인 인공신경망 모형(Artificial Neural Network Model)과 가뭄지수를 이용하여 남한지역의 서울, 대전, 대구, 광주 등의 4개 기상관측소를 선정하여 가뭄예측을시도하였다. 가뭄 예측을 위하여 남한지역 내 선정한 기상관측소의 관측된 과거 강수량 자료를 이용하여 산정된 SPI (Standardized Precipitation Index)를 입력변수로 하여 다층 퍼셉트론(Multi Layer Perceptron) 인공신경망 모델에 적용하였으며, 매개변수 보정을 위한 학습기간으로 1976~2000년과 2001~2010년을 예측을 위한 검증기간으로 선정하여, 학습 및 예측을 시도하였다. 학습된 최적의 예측모형을 이용하여 서로 다른 선행예보시간(1~6개월)을 갖고 SPI (3), SPI (6), SPI (12)별로 가뭄을 예측하였으며, 가뭄예측 결과, SPI (3)의 경우에는 1개월 선행예보에서만 좋은 결과를 나타내었으며, SPI (6)의 경우 1~3개월 후의 가뭄을 예측하는 경우에 비교적 관측자료와 잘 일치하는 결과를 나타내었다. SPI (12)의 경우에는 약5개월 후까지의 가뭄예측에 양호한 결과를 나타내었다.

• Ocean and Polar Research
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• 제34권2호
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• pp.185-195
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• 2012

This paper focused on forecasting a short-term production of oysters, which have been farmed in Korea, with distinct periodicity of production by year, and different production level by month. To forecast a short-term oyster production, this paper uses monthly data (260 observations) from January 1990 to August 2011, and also adopts several econometrics methods, such as Multiple Regression Analysis Model (MRAM), Seasonal Autoregressive Integrated Moving Average (SARIMA) Model, and Vector Error Correction Model (VECM). As a result, first, the amount of short-term oyster production forecasted by the multiple regression analysis model was 1,337 ton with prediction error of 246 ton. Secondly, the amount of oyster production of the SARIMA I and II models was forecasted as 12,423 ton and 12,442 ton with prediction error of 11,404 ton and 11,423 ton, respectively. Thirdly, the amount of oyster production based on the VECM was estimated as 10,425 ton with prediction errors of 9,406 ton. In conclusion, based on Theil inequality coefficient criterion, short-term prediction of oyster by the VECM exhibited a better fit than ones by the SARIMA I and II models and Multiple Regression Analysis Model.

• 상하수도학회지
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• 제11권1호
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• pp.109-118
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• 1997

In this study, we examined the structural analysis of water demand fluctuation for water distribution control of water supply network. In order to analyze for the length of stationary time series, we calculate autocorrelation coefficient of each case equally divided data size. As a result, it was found that, with the data size of around three months, any case could be used as stationary time series. we analyze cross-correlation coefficient between the daily water consumption's data and primary influence factors. As a result, we have decided to use weather conditions and maximum temperature as natural primary factors and holidays as a social factor. Applying the multiple ARIMA model, we obtains an effective model to describe the daily water demand prediction. From the forecasting result, even though we forecast water distribution quantity of the following year, estimated values well express the flctuations of measurements. Thus, the suitability of the model for practical use can be confirmed. When this model is used for practical water distribution control, water distribution quantity for the following day should be found by inputting maximum temperature and weather conditions obtained from weather forecast, and water purification plants and service reservoirs should be operated based on this information while operation of pumps and valves should be set up. Consequently, we will be able to devise a rational water management system.

• 산업경영시스템학회지
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• 제41권1호
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• pp.84-93
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• 2018

In this paper, we propose an Elman recurrent neural network to predict and analyze a time series of power energy consumption. To this end, we consider the volatility of the time series and apply the sample variance and the detrended fluctuation analyses to the volatilities. We demonstrate that there exists a correlation in the time series of the volatilities, which suggests that the power consumption time series contain a non-negligible amount of the non-linear correlation. Based on this finding, we adopt the Elman recurrent neural network as the model for the prediction of the power consumption. As the simplest form of the recurrent network, the Elman network is designed to learn sequential or time-varying pattern and could predict learned series of values. The Elman network has a layer of "context units" in addition to a standard feedforward network. By adjusting two parameters in the model and performing the cross validation, we demonstrated that the proposed model predicts the power consumption with the relative errors and the average errors in the range of 2%~5% and 3kWh~8kWh, respectively. To further confirm the experimental results, we performed two types of the cross validations designed for the time series data. We also support the validity of the model by analyzing the multi-step forecasting. We found that the prediction errors tend to be saturated although they increase as the prediction time step increases. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric and the gas energies.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.166-171
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• 2023

The Stock price analysis is an increasing concern in a financial time series. The purpose of the study is to analyze the price parameters of date, high, low, and news feed about the stock exchange price. Long short term memory (LSTM) is a cutting-edge technology used for predicting the data based on time series. LSTM performs well in executing large sequence of data. This paper presents the Long Short Term Memory Model has used to analyze the stock price ranges of 10 days and 20 days by exponential moving average. The proposed approach gives better performance using technical indicators of stock price with an accuracy of 82.6% and cross entropy of 71%.

• 대한교통학회지
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• 제22권5호
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• pp.89-97
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• 2004

현재 군 수송기는 매주 일정 대수가 각 기지 간 물자수송, 인원수송 그리고 조종사들의 훈련목적으로 정해진 스케줄에 의해 운항하고 있다. 일일 수송기 운행 경로는 모 기지에서 이륙하여 몇 개의 기지를 경유하여 임무를 수행한 후, 다시 모 기지로 돌아오는 패턴을 취하고 있다. 본 연구는 공군의 중앙물자를 저장하는 창에서 각 기지로 물자를 수송할 때 물량예측 및 적정노선에 대해서 시계열분석과 차량경로모형이라는 두 가지 방법을 통해 접근한다. 먼저 현재 공군에서 사용하고 있는 각종 규정과 교범상의 항공수송에 대한 제약사항 자료를 수집하여, 이를 바탕으로 본 연구에서는 다회방문이 가능하고, 경우회수에 대한 제약을 갖는 모형시과 알고리즘을 제안하였다. 또한 지난 몇 년간의 수송물량을 시계열 분석을 이용하여 예측하고, 예측된 수송소요를 제안된 알고리즘에 적용하여 적정노선을 계획하는 방법을 제안하였다. 제안된 모형과 알고리즘의 적합성과 경제성을 파악하기 위해 본 연구에서는 모형에서 계산된 수송물량과 필요 수송기 대수와 실제 공군에서 수행된 항공화물 수송 물량과 투입 수송기 대수를 비교함으로써 객관적인 모형의 우수성을 입증하고자 하였다.

• 한국컴퓨터그래픽스학회논문지
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• 제23권4호
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• pp.21-29
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• 2017

예측 시각적 분석 연구는 다양한 대화식 데이터 탐색 기법을 사용하여 예측 결과의 불확실성을 줄이는데 중점을 두었다. 대화식 탐색 기법의 목적은 변수간의 관계를 이해하고 알려지지 않은 변수를 예측하기 위한 적합한 모델을 선택함으로서 의사결정권자의 수준에 따른 예측결과의 품질 차이를 줄이는 것이다. 하지만 청소년 신체 성장 데이터와 같이 전체적인 추세가 알려지지 않은 시계열 데이터를 설명할 수 있는 예측 모델을 만드는 것은 어렵다. 본 논문에서는 불확실한 추세를 가지는 시계열 데이터 단편에서 물리적 성장 값을 예측하기 위한 새로운 예측 방법을 제안한다. 새로운 예측 방법은 특정 시점에서의 데이터 분포를 추정하는 방법으로 실험결과 기존 회귀 모델보다 높은 정확도를 갖는다. 또한 우리는 예측 모델링 과정에서 발생 가능한 불확실성을 최소화 할 수 있는 시각적 분석 방법을 제안한다.

• 대한조선학회논문집
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• 제58권5호
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• pp.281-293
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• 2021

Oil and steel prices, which are major pricescosts in the shipbuilding industry, were predicted. Firstly, the error of the moving average line (N=3-5) was examined, and in all three error analyses, the moving average line (N=3) was small. Secondly, in the linear prediction of data through existing theory, oil prices rise slightly, and steel prices rise sharply, but in reality, linear prediction using existing data was not satisfactory. Thirdly, we identified the limitations of linear prediction methods and confirmed that oil and steel price prediction was somewhat similar to actual moving average line prediction methods. Due to the high volatility of major price flows, large errors were inevitable in the forecast section. Through the time series analysis method at the end of this paper, we were able to achieve not bad results in all analysis items relative to artificial intelligence (Prophet). Predictive data through predictive analysis using eight predictive models are expected to serve as a good research foundation for developing unique tools or establishing evaluation systems in the future. This study compares the basic settings of artificial intelligence programs with the results of core price prediction in the shipbuilding industry through time series prediction theory, and further studies the various hyper-parameters and event effects of Prophet in the future, leaving room for improvement of predictability.