• 산업기술연구
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• 제29권B호
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• pp.9-15
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• 2009

In the case of traditional binary encoding technique, it takes long time to converge the optimal solutions and brings about complexity of the systems due to encoding and decoding procedures. However, the ROGAs (real-coded genetic algorithms) do not require these procedures, and the k-means clustering algorithm can avoid global searching space. Thus, this paper proposes a new approach by using their advantages. The proposed method constructs the multiple predictors using the optimal differences that can reveal the patterns better and properties concealed in non-stationary time series where the k-means clustering algorithm is used for data classification to each predictor, then selects the best predictor. After selecting the best predictor, the cluster centers of the predictor are tuned finely via RCGKA in secondary tuning procedure. Therefore, performance of the predictor can be more enhanced. Finally, we verifies the prediction performance of the proposed system via simulating typical time series examples.

• 한국해양공학회지
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• 제36권5호
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• pp.295-302
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• 2022

The initial response to a marine accident can play a key role to minimize the accident. Therefore, various decision support systems have been developed using sensors, simulations, and active response equipment. In this study, we developed an algorithm to predict damage locations using ship motion data with bidirectional long short-term memory (BiLSTM), a type of recurrent neural network. To reflect the low frequency ship motion characteristics, 200 time-series data collected for 100 s were considered as input values. Heave, roll, and pitch were used as features for the prediction model. The F1-score of the BiLSTM model was 0.92; this was an improvement over the F1-score of 0.90 of a prior model. Furthermore, 53 of 75 locations of damage had an F1-score above 0.90. The model predicted the damage location with high accuracy, allowing for a quick initial response even if the ship did not have flood sensors. The model can be used as input data with high accuracy for a real-time progressive flooding simulator on board.

• 한국항해학회지
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• 제24권3호
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• pp.147-155
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• 2000

In this paper, we propose the Chaos Fuzzy controller to analyze the chaotic character of time series obtained from the specific plant and to predict the short-term for power consumption of the plant using the Fuzzy controller. We compared the predicted data with the active ones and checked the error generated by them after we time series of supplied power to the proposed controller. As a result of the simulation, we obtained a admirable consequence that the proposed controller can be advanced through various and accurate data acquisition, and continuous analysis of the resident and industrial environment.

• Journal of the Korean Data and Information Science Society
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• 제17권1호
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• pp.141-147
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• 2006

BOK-X12-ARIMA has been developed by the Bank of Korea in order to accomodate special features such as lunar effect, labor day and election effect which are intrinsic in Korean seasonal time series. Irregular component resulting from BOK-X12-ARIMA is usually treated as white noise time series. If this shows dependency structure, it may be advisable to incorporate dependency in irregular component into prediction. This article illustrates how to refine point forecast using dependency structure in irregular component.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제11권7호
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• pp.307-314
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• 2022

최근 전력수요를 예측하기 위해 통계기반 시계열 분석 기법을 대체하기 위해 딥러닝 기법을 활용한 연구가 활발히 진행되고 있다. 딥러닝 기반 전력수요 예측 연구 결과를 분석한 결과, LSTM 기반 예측 모델의 성능이 우수한 것으로 규명되었으나 장기간의 지역 범위 전력수요 예측에 대해 LSTM 기반 모델의 성능이 충분하지 않음을 확인할 수 있다. 본 연구에서는 기온 데이터를 반영하여 24시간 이전에 전력수요를 예측하는 WaveNet 기반 딥러닝 모델을 개발하여, 실제 사용하고 있는 통계적 시계열 예측 기법의 정확도(MAPE 값 2%)보다 우수한 예측 성능을 달성하는 모델을 개발하고자 한다. 먼저 WaveNet의 핵심 구조인 팽창인과 1차원 합성곱 신경망 구조를 소개하고, 전력수요와 기온 데이터를 입력값으로 모델에 주입하기 위한 데이터 전처리 과정을 제시한다. 다음으로, 개선된 WaveNet 모델을 학습하고 검증하는 방법을 제시한다. 성능 비교 결과, WaveNet 기반 모델에 기온 데이터를 반영한 방법은 전체 검증데이터에 대해 MAPE 값 1.33%를 달성하였고, 동일한 구조의 모델에서 기온 데이터를 반영하지 않는 것(MAPE 값 2.31%)보다 우수한 전력수요 예측 결과를 나타내고 있음을 확인할 수 있다.

• 한국정보전자통신기술학회논문지
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• 제17권3호
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• pp.176-182
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• 2024

본 연구는 결측치 비율이 높은 시계열 데이터를 효과적으로 분석하고 예측할 수 있는 머신러닝 모델을 구축하기 위해 다양한 결측치 처리 방법을 비교 분석하였다. 이를 위해 PSMF(Predictive State Model Filtering), MissForest, IBFI(Imputation By Feature Importance) 방법을 적용하였으며, 이후 LightGBM, XGBoost, EBM(Explainable Boosting Machines) 머신러닝 모델을 사용하여 예측 성능을 평가하였다. 연구 결과, 결측치 처리 방법 중에서는 MissForest와 IBFI가 비선형적 데이터 패턴을 잘 반영하여 가장 높은 성능을 나타냈으며, 머신러닝 모델 중에서는 XGBoost와 EBM 모델이 LightGBM 모델보다 더 높은 성능을 보였다. 본 연구는 결측치 비율이 높은 시계열 데이터의 분석 및 예측에 있어 비선형적 결측치 처리 방법과 머신러닝 모델의 조합이 중요함을 강조하며, 실무적으로 유용한 방법론을 제시하였다.

• 품질경영학회지
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• 제48권3호
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• pp.511-520
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• 2020

Purpose: The purpose of this study was to devise an accurate machine learning model for predicting silica concentrations following the addition of impurities, through time series analysis of mining data. Methods: The mining data were preprocessed and subjected to time series analysis using the machine learning model. Through correlation analysis, valid variables were selected and meaningless variables were excluded. To reflect changes over time, dependent variables at baseline were treated as independent variables at later time points. The relationship between independent variables and the dependent variable after n point was subjected to Pearson correlation analysis. Results: The correlation (R2) was strongest after 3 hours, which was adopted as a dependent variable. According to root mean square error (RMSE) data, the proposed method was superior to the other machine learning methods. The XGboost algorithm showed the best predictive performance. Conclusion: This study is important given the current lack of machine learning studies pertaining to the domestic mining industry. In addition, using time series analysis in mining data will show further improvement. Before establishing a predictive model for the proposed method, predictions should be made using data with time series characteristics. After doing this work, it should also improve prediction accuracy in other domains.

• 인터넷정보학회논문지
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• 제24권2호
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• pp.67-74
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• 2023

금융 분야에서 주가예측연구는 거래 안정성 및 이익 실현 등을 목적으로 한다. 기존의 통계적 예측기법은 무작위로 예측한 결과와 정확도 측면에서 비슷하거나 낮은 예측 신뢰도 때문에 실제 거래 결정에 참고 되기 어렵다. 인공지능 모델은 데이터특성과 변동패턴을 학습해 예측하기 때문에 향상된 정확도를 달성한다. 그러나 장기간의 시계열 데이터를 사용해 주가를 예측하는 것은 여전히 어려운 문제이다. 본 논문에서는 K-means 클러스터링 기반의 데이터 증강 및 입력 시퀀스의 Window-size 별 정규화 기법과 시계열 학습에 특화된 LSTM 모델을 활용하여 안정적이고 신뢰성 있는 주가예측 방법을 제안한다. 이를 통해 더욱 정확하고 신뢰성 있는 예측 결과를 얻고, 나아가 시장 안정성에 기여할 뿐 아니라 높은 수익도 추구할 수 있다.

• ETRI Journal
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• 제43권6호
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• pp.1058-1080
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• 2021

Crime is not a completely random event but rather shows a pattern in space and time. Capturing the dynamic nature of crime patterns is a challenging task. Crime prediction models that rely only on neighborhood influence and demographic features might not be able to capture the dynamics of crime patterns, as demographic data collection does not occur frequently and is static. This work proposes a novel approach for crime count and hotspot prediction to capture the dynamic nature of crime patterns using taxi data along with historical crime and demographic data. The proposed approach predicts crime events in spatial units and classifies each of them into a hotspot category based on the number of crime events. Four models are proposed, which consider different covariates to select a set of independent variables. The experimental results show that the proposed combined subset model (CSM), in which static and dynamic aspects of crime are combined by employing the taxi dataset, is more accurate than the other models presented in this study.

• 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.