• Journal of Positioning, Navigation, and Timing
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• 제9권2호
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• pp.89-98
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• 2020

Prediction of clock behaviors is necessary to generate very high stable system time which is essential for a satellite navigation system. For the purpose, we applied the Auto-Regressive Integrated Moving Average (ARIMA) model to the prediction of two hydrogen masers' behaviors with respect to the rapid Coordinated Universal Time (UTCr). Using the packaged programming language R, we made an analysis and prediction of time series data of [UTCr - clocks]. The maximum variation width of the residuals which were obtained by the difference between the predicted and measured values, was 6.2 ns for 106 days. This variation width was just one-sixth of [UTCr-UTC (KRIS)] published by the BIPM for the same period. Since the two hydrogen masers were found to be strongly correlated, we applied the Vector Auto-Regressive Moving Average (VARMA) model for more accurate prediction. The result showed that the prediction accuarcy was improved by two times for one hydrogen maser.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.135-135
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• 2021

In water resources management, rainfall prediction with high accuracy is still one of controversial issues particularly in countries facing heavy rainfall during wet seasons in the monsoon climate. The aim of this study is to develop an artificial neural network (ANN) for predicting future six months of rainfall data (from April to September 2020) from daily meteorological data (from 1971 to 2019) such as rainfall, temperature, wind speed, and humidity at Seoul, Korea. After normalizing these data, they were trained by using a multilayer perceptron (MLP) as a class of the feedforward ANN with 15,000 neurons. The results show that the proposed method can analyze the relation between meteorological datasets properly and predict rainfall data for future six months in 2020, with an overall accuracy over almost 70% and a root mean square error of 0.0098. This study demonstrates the possibility and potential of MLP's applications to predict future daily rainfall patterns, essential for managing flood risks and protecting water resources.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2017년도 추계학술대회
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• pp.50-53
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• 2017

장기 해운불황에 따라 불확실성이 증폭되고 있는 상황에서 경기추세의 이해뿐만 아니라 예측 또한 중요해지고 있는 실정이다. 본 논문에서는 최근 특정 복잡한 문제에 대해서 각광받고 있는 인공신경망을 적용하여 BDI 예측을 연구하였다. 사용된 인공신경망은 순환신경망으로 RNN과 LSTM 그리고 비교의 목적으로 MLP를 통해 2009.04.01.부터 2017.07.31.의 기간을 대상으로 연구를 진행하였다. 또한 전통적 시계열 예측방법론인 ARIMA 분석을 실시해 인공신경망들의 예측성능과 비교하였다. 결과로 순환신경망인 RNN의 성능이 가장 뛰어났으며 LSTM의 특정 시계열(BDI)에의 적용가능성을 확인할 수 있었다.

• 산업경영시스템학회지
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• 제39권1호
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• pp.64-72
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• 2016

In this paper, we utilize a Gaussian process to predict the power consumption in the air-conditioning system. As the power consumption in the air-conditioning system takes a form of a time-series and the prediction of the power consumption becomes very important from the perspective of the efficient energy management, it is worth to investigate the time-series model for the prediction of the power consumption. To this end, we apply the Gaussian process to predict the power consumption, in which the Gaussian process provides a prior probability to every possible function and higher probabilities are given to functions that are more likely consistent with the empirical data. We also discuss how to estimate the hyper-parameters, which are parameters in the covariance function of the Gaussian process model. We estimated the hyper-parameters with two different methods (marginal likelihood and leave-one-out cross validation) and obtained a model that pertinently describes the data and the results are more or less independent of the estimation method of hyper-parameters. We validated the prediction results by the error analysis of the mean relative error and the mean absolute error. The mean relative error analysis showed that about 3.4% of the predicted value came from the error, and the mean absolute error analysis confirmed that the error in within the standard deviation of the predicted value. We also adopt the non-parametric Wilcoxon's sign-rank test to assess the fitness of the proposed model and found that the null hypothesis of uniformity was accepted under the significance level of 5%. These results can be applied to a more elaborate control of the power consumption in the air-conditioning system.

• 지능정보연구
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• 제4권1호
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• pp.133-147
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• 1998

Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deterministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or ARIMA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministically chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model. This result shows that the forecasting model a, pp.opriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.

• 한국지능시스템학회논문지
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• 제19권5호
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• pp.689-694
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• 2009

본 논문은 일별 및 주별로 시계열 주가를 예측할 수 있는 퍼지 모델을 구성하는 방법을 제안한다. 전통적인 시계열 분석으로 주가를 예측하는 것은 어렵지만 퍼지 모델은 비선형적인 주가 데이터의 특성을 잘 기술할 수 있는 장점을 갖고 있다. 주가 예측 모델에 사용될 입력 정보를 결정하는 데는 상당한 수고가 필요한데, 본 논문에서는 전통적인 캔들 스틱 차트의 정보를 입력변수로 고려한다. 주가 예측 퍼지 모델은 사다리꼴 멤버쉽함수를 갖는 전건부와 비선형식인 후건부로 된 퍼지 규칙으로 구성된다. 차분 진화를 통해 퍼지 모델은 최적화된다. 일별 및 주별로 코스피 지수의 시가, 고가, 저가 및 종가를 예측하는 모델을 만들고 그 성능을 평가한다.

• 스마트미디어저널
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• 제10권2호
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• pp.16-21
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• 2021

금융 시계열 분석은 현대 사회의 경제적, 사회적으로 매우 중요한 역할을 하며 세계 발전에 영향을 미치는 중요한 과제지만 많은 잡음(noise)과 불확실성 등의 어려움으로 인해 금융 시계열 분석 예측은 어려운 연구 주제이다. 본 논문에서는 비정형 데이터와 정형 데이터를 함께 이미지로 변환하여 시장을 예측 하는 방법(MPIL)을 제안한다. 시장 예측을 위해 n일 기간의 비정형 데이터인 SNS, 뉴스 데이터를 감정분석하고 정형 데이터인 시장 데이터를 GADF 알고리즘으로 이미지 변환하고 이미지 학습을 통해 n+1일의 가격을 예측하는 초단기 시장을 예측한다. MPIL은 평균 정확도 56%로 기존 시장예측에 사용되던 감정분석을 활용하여 LSTM으로 시장을 예측하는 모델 평균 정확도 50%보다 높은 정확도를 보였다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.122-127
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• 2001

This study investigates the effectiveness of time delay neural networks(TDNN) for the time dependent prediction domain. Although it is well-known fact that the back-propagation neural network(BPN) performs well in pattern recognition tasks, the method has some limitations in that it can only learn an input mapping of static (or spatial) patterns that are independent of time of sequences. The preliminary results show that the accuracy of TDNN is higher than the standard BPN with time lag. Our proposed approaches are demonstrated by the stork market prediction domain.

• 한국항행학회논문지
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• 제11권3호
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• pp.252-258
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• 2007

무선모바일 통신망에서는 사용자의 이동성보장 기술과 사용자가 요구하는 서비스품질(QoS)을 만족시키기 위한 효율적인 무선자원관리기술이 많이 연구되어 왔다. 본 연구에서는 시계열 예측기법(Time series prediction) 인 ARMA(Auto Regressive Moving Average) 모델을 이용하여 사용자가 요구하는 자원의 양을 예측하여 동적으로 자원을 할당함으로써 사용자의 이동성에 따른 QoS를 보장할 수 있는 자원할당방법을 제안한다. 제안한 방법은 ARMA 예측모델을 사용하여 이전에 핸드오프연결이 사용한 채널 수를 기초로 앞으로 필요로 하는 채널 수를 예측하여 예약함으로써 원하는 핸드오프 손실률에서 서비스가 이루어지도록 한다. 시뮬레이션을 통하여 기존의 RCS(Reserved channel scheme) 방법과 비교하여 핸드오프 연결의 손실률과 자원의 이용률에서 우수함을 보인다.

• 한국멀티미디어학회논문지
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• 제25권1호
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• pp.82-91
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• 2022

In this paper, time series prediction method of photovoltaic power is introduced using seasonal autoregressive integrated moving average (SARIMA). In order to obtain the best fitting model by a time series method in the absence of an environmental sensor, this research was used data below 50% of cloud cover. Three samples were extracted by time intervals from the raw data. After that, the best fitting models were derived from mean absolute percentage error (MAPE) with the minimum akaike information criterion (AIC) or beysian information criterion (BIC). They are SARIMA (1,0,0)(0,2,2)14, SARIMA (1,0,0)(0,2,2)28, SARIMA (2,0,3)(1,2,2)55. Generally parameter of model derived from BIC was lower than AIC. SARIMA (2,0,3)(1,2,2)55, unlike other models, was drawn by AIC. And the performance of models obtained by SARIMA was compared. MAPE value was affected by the seasonal period of the sample. It is estimated that long seasonal period samples include atmosphere irregularity. Consequently using 1 hour or 30 minutes interval sample is able to be helpful for prediction accuracy improvement.