• Title/Summary/Keyword: Time-series data prediction

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The Comparison of Parameter Estimation and Prediction Methods for STBL Model

  • Kim, Duk-Gi;Kim, Sung-Soo;Lee, Chan-Hee;Lee, Keon-Myung;Lee, Sung-Duck
    • Journal of the Korean Data and Information Science Society
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    • v.18 no.1
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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.

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Prediction of Time Histories of Seismic Ground Motion using Genetic Programming

  • YOSHIHARA, Ikuo;Inaba, Masaaki;AOYAMA, Tomoo;Yasunaga, Moritoshi
    • 제어로봇시스템학회:학술대회논문집
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    • 1999.10a
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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.

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Chaotic Time Series Prediction using Parallel-Structure Fuzzy Systems (병렬구조 퍼지스스템을 이용한 카오스 시계열 데이터 예측)

  • 공성곤
    • Journal of the Korean Institute of Intelligent Systems
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    • v.10 no.2
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    • pp.113-121
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    • 2000
  • This paper presents a parallel-structure fuzzy system(PSFS) for prediction of time series data. The PSFS consists of a multiple number of fuzzy systems connected in parallel. Each component fuzzy system in the PSFS predicts the same future data independently based on its past time series data with different embedding dimension and time delay. The component fuzzy systems are characterized by multiple-input singleoutput( MIS0) Sugeno-type fuzzy rules modeled by clustering input-output product space data. The optimal embedding dimension for each component fuzzy system is chosen to have superior prediction performance for a given value of time delay. The PSFS determines the final prediction result by averaging the outputs of all the component fuzzy systems excluding the predicted data with the minimum and the maximum values in order to reduce error accumulation effect.

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Prediction of arrhythmia using multivariate time series data (다변량 시계열 자료를 이용한 부정맥 예측)

  • Lee, Minhai;Noh, Hohsuk
    • The Korean Journal of Applied Statistics
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    • v.32 no.5
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    • pp.671-681
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    • 2019
  • Studies on predicting arrhythmia using machine learning have been actively conducted with increasing number of arrhythmia patients. Existing studies have predicted arrhythmia based on multivariate data of feature variables extracted from RR interval data at a specific time point. In this study, we consider that the pattern of the heart state changes with time can be important information for the arrhythmia prediction. Therefore, we investigate the usefulness of predicting the arrhythmia with multivariate time series data obtained by extracting and accumulating the multivariate vectors of the feature variables at various time points. When considering 1-nearest neighbor classification method and its ensemble for comparison, it is confirmed that the multivariate time series data based method can have better classification performance than the multivariate data based method if we select an appropriate time series distance function.

Prediction System Design based on An Interval Type-2 Fuzzy Logic System using HCBKA (HCBKA를 이용한 Interval Type-2 퍼지 논리시스템 기반 예측 시스템 설계)

  • Bang, Young-Keun;Lee, Chul-Heui
    • Journal of Industrial Technology
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    • v.30 no.A
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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.

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Prediction of Sunspot Number Time Series using the Parallel-Structure Fuzzy Systems (병렬구조 퍼지시스템을 이용한 태양흑점 시계열 데이터의 예측)

  • Kim Min-Soo;Chung Chan-Soo
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.54 no.6
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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.

A Study on the Prediction of Major Prices in the Shipbuilding Industry Using Time Series Analysis Model (시계열 분석 모델을 이용한 조선 산업 주요물가의 예측에 관한 연구)

  • Ham, Juh-Hyeok
    • Journal of the Society of Naval Architects of Korea
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    • v.58 no.5
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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.

The Data Processing Method for Small Samples and Multi-variates Series in GPS Deformation Monitoring

  • Guo-Lin, Liu;Wen-Hua, Zheng;Xin-Zhou, Wang;Lian-Peng, Zhang
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • v.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.

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Implementation of CNN-based water level prediction model for river flood prediction (하천 홍수 예측을 위한 CNN 기반의 수위 예측 모델 구현)

  • Cho, Minwoo;Kim, Sujin;Jung, Hoekyung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.25 no.11
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    • pp.1471-1476
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    • 2021
  • Flood damage can cause floods or tsunamis, which can result in enormous loss of life and property. In this regard, damage can be reduced by making a quick evacuation decision through flood prediction, and many studies are underway in this field to predict floods using time series data. In this paper, we propose a CNN-based time series prediction model. A CNN-based water level prediction model was implemented using the river level and precipitation, and the performance was confirmed by comparing it with the LSTM and GRU models, which are often used for time series prediction. In addition, by checking the performance difference according to the size of the input data, it was possible to find the points to be supplemented, and it was confirmed that better performance than LSTM and GRU could be obtained. Through this, it is thought that it can be utilized as an initial study for flood prediction.

Time-series Analysis and Prediction of Future Trends of Groundwater Level in Water Curtain Cultivation Areas Using the ARIMA Model (ARIMA 모델을 이용한 수막재배지역 지하수위 시계열 분석 및 미래추세 예측)

  • Baek, Mi Kyung;Kim, Sang Min
    • Journal of The Korean Society of Agricultural Engineers
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    • v.65 no.2
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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.