• Journal of the Korean Data and Information Science Society
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• 제18권1호
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• pp.17-29
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• 2007

The major purpose of this article is the comparison of estimation method with Newton-Raphson, Kalman-filter, and prediction method with Kalman prediction. Conditional expectation in space time bilinear(STBL) model, which is a very powerful and parsimonious nonlinear time-series model for the space time series data can be viewed as a set of time series collected simultaneously at a number of spatial locations and time points, and which have appeared in a important applications areas: geography, geology, natural resources, ecology, epidemiology, etc.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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• pp.226-229
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• 1999

We have been developing a method to build models for time series using Genetic Programming. The proposed method has been applied to various kinds of time series e.g. computer-generated chaos, natural phenomena, and financial market indices etc. Now we apply the prediction method to time histories of seismic ground motion i.e. one-step-ahead prediction of seismographic amplitude. Waves of earthquakes are composed of P-waves and S-waves. They propagate in different speeds and have different characteristics. It is believed that P-waves arrive firstly and S-waves arrive secondly. Simulations were performed based on real data of Hyuganada earthquake which broke out at southern part of Kyushuu Island in Japan. To our surprise, prediction model built using the earthquake waves in early time can enough precisely predict main huge waves in later time. Lots of experiments lead us to conclude that every slice of data involves P-wave and S-wave. The simulation results suggest the GP-based prediction method can be utilized in alarm systems or dispatch systems in an emergency.

• 한국지능시스템학회논문지
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• 제10권2호
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• pp.113-121
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• 2000

이 논문에서는 병렬구조 퍼지시스템(PSFS)에 기초한 카오스 시계열 데이터의 예측 알고리즘에 대해 연구하였다 병렬구조 퍼지시스템은 병렬로 연결된 여러개의 퍼지시스템에 의하여 구성되어있다. 병렬구조 퍼지시스템을 구성하고 있는 각 퍼지시스템은 다른 임베딩 차원과 시간지연을 가지고 과거의 데이터를 이용하여 동일한 데이터를 독립적으로 예측한다 퍼지시스템은 입출력 데이터를 클러스터링하여 모델링되는 MISO Sugeno 퍼지규칙에 의하여 특징지어진다. 각 퍼지시스템에 대한 최적 임베딩차원은 주어진 시간지연값에 대해서 최적의 성능을 갖도록 선정된다. 병렬구조 퍼지시스템은 각 구성요소 퍼지스템들의 예측값중에서 최대값과 최소값을 가지는 예측결과를 제외하고 나머지 값들을 평균하여 최종 예측 결과를 얻는다.

• 응용통계연구
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• 제32권5호
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• pp.671-681
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• 2019

최근에 부정맥 환자가 증가하면서 머신러닝을 이용한 부정맥을 예측하는 연구가 활발하게 진행되고 있다. 기존의 많은 연구들은 특정한 시점의 RR 간격 데이터에서 추출한 특징변수 다변량 데이터에 기반하여 부정맥을 예측하였다. 본 연구에서는 심장 상태가 시간에 따라 변해가는 패턴도 부정맥 예측에 중요한 정보가 될 수 있다고 생각하여 일정한 시간 간격을 두고 특징변수의 다변량 벡터를 추출하여 쌓음으써 얻어지는 다변량 시계열 데이터로 부정맥을 예측하는 것의 유용성에 대해 살펴보았다. 1-Nearest Neighbor 방법과 그것을 앙상블(ensemble)한 learner를 중심으로 비교했을 경우 시계열의 특징을 고려한 적절한 시계열 거리함수를 선택하여 시계열 정보를 활용한 다변량 시계열 데이터 기반 방법의 분류 성능이 더 좋게 나오는 것을 확인하였다.

• 산업기술연구
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• 제30권A호
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• pp.111-117
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• 2010

To improve the performance of the prediction system, the system should reflect well the uncertainty of nonlinear data. Thus, this paper presents multiple prediction systems based on Type-2 fuzzy sets. To construct each prediction system, an Interval Type-2 TSK Fuzzy Logic System and difference data were used, because, in general, it has been known that the Type-2 Fuzzy Logic System can deal with the uncertainty of nonlinear data better than the Type-1 Fuzzy Logic System, and the difference data can provide more steady information than that of original data. Also, to improve each rule base of the fuzzy prediction systems, the HCBKA (Hierarchical Correlation Based K-means clustering Algorithm) was applied because it can consider correlationship and statistical characteristics between data at a time. Subsequently, to alleviate complexity of the proposed prediction system, a system selection method was used. Finally, this paper analyzed and compared the performances between the Type-1 prediction system and the Interval Type-2 prediction system using simulations of three typical time series examples.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권6호
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• pp.390-395
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• 2005

Sunspots are dark areas that grow and decay on the lowest level of the sun that is visible from the Earth. Shot-term predictions of solar activity are essential to help plan missions and to design satellites that will survive for their useful lifetimes. This paper presents a parallel-structure fuzzy system(PSFS) for prediction of sunspot number time series. The PSFS consists of a multiple number of component fuzzy systems connected in parallel. Each component fuzzy system in the PSFS predicts future data independently based on its past time series data with different embedding dimension and time delay. An embedding dimension determines the number of inputs of each component fuzzy system and a time delay decides the interval of inputs of the time series. According to the embedding dimension and the time delay, the component fuzzy system takes various input-output pairs. The PSFS determines the final predicted value as an average of all the outputs of the component fuzzy systems in order to reduce error accumulation effect.

• 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.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.185-189
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• 2006

Time series analysis is a frequently effective method of constructing model and prediction in data processing of deformation monitoring. The monitoring data sample must to be as more as possible and time intervals are equal roughly so as to construct time series model accurately and achieve reliable prediction. But in the project practice of GPS deformation monitoring, the monitoring data sample can't be obtained too much and time intervals are not equal because of being restricted by all kinds of factors, and it contains many variates in the deformation model moreover. It is very important to study the data processing method for small samples and multi-variates time series in GPS deformation monitoring. A new method of establishing small samples and multi-variates deformation model and prediction model are put forward so as to resolve contradiction of small samples and multi-variates encountered in constructing deformation model and improve formerly data processing method of deformation monitoring. Based on the system theory, a deformation body is regarded as a whole organism; a time-dependence linear system model and a time-dependence bilinear system model are established. The dynamic parameters estimation is derived by means of prediction fit and least information distribution criteria. The final example demonstrates the validity and practice of this method.

• 한국정보통신학회논문지
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• 제25권11호
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• pp.1471-1476
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• 2021

수해는 홍수나 해일을 유발하여 막대한 인명과 재산의 피해를 초래할 수 있다. 이에 대해 홍수 예측을 통한 빠른 대피 결정으로 피해를 줄일 수 있으며, 해당 분야에서는 시계열 데이터를 활용하여 홍수를 예측하려는 연구들도 많이 진행되고 있다. 본 논문에서는 CNN 기반의 시계열 예측 모델을 제안한다. 하천의 수위와 강수량을 사용하여 CNN 기반의 수위 예측 모델을 구현하였고, 시계열 예측에 많이 사용되는 LSTM, GRU 모델과 비교하여 성능을 확인하였다. 또한 입력 데이터의 크기에 따른 성능 차이를 확인하여 보완해야 할 점을 찾을 수 있었고, LSTM과 GRU보다 더 좋은 성능을 낼 수 있다는 것을 확인하였다. 이를 통해 홍수 예측을 위한 초기 연구로서 활용할 수 있을 것으로 사료된다.

• 한국농공학회논문집
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• 제65권2호
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• pp.1-11
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• 2023

This study analyzed the impact of greenhouse cultivation area and groundwater level changes due to the water curtain cultivation in the greenhouse complexes. The groundwater observation data in the Miryang study area were used and classified into greenhouse and field cultivation areas to compare the groundwater impact of water curtain cultivation in the greenhouse complex. We identified the characteristics of the groundwater time series data by the terrain of the study area and selected the optimal model through time series analysis. We analyzed the time series data for each terrain's two representative groundwater observation wells. The Seasonal ARIMA model was chosen as the optimal model for riverside well, and for plain and mountain well, the ARIMA model and Seasonal ARIMA model were selected as the optimal model. A suitable prediction model is not limited to one model due to a change in a groundwater level fluctuation pattern caused by a surrounding environment change but may change over time. Therefore, it is necessary to periodically check and revise the optimal model rather than continuously applying one selected ARIMA model. Groundwater forecasting results through time series analysis can be used for sustainable groundwater resource management.