• Title/Summary/Keyword: time series

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Fuzzy time-series model of fuzzy number observations (퍼지 넘버 연산에 의한 퍼지 시계열 모형)

  • Hong, Dug-Hun
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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    • pp.139-144
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    • 2000
  • Recently, a homogeneous fuzzy time series model was proposed by means of defining some new operations on fuzzy numbers. In this paper, we consider expanding the results to the nonhomogeneous fuzzy time series and the general fuzzy time series using Tw, the weakest t-norm, based algebraic fuzzy operations.

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Estimation of Parameters in Fuzzy Time Series Model with Triangular Fuzzy Numbers

  • Shon Eun Hee;Sohn Keon Tae
    • Proceedings of the Korean Statistical Society Conference
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    • 한국통계학회 2000년도 추계학술발표회 논문집
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    • pp.267-269
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    • 2000
  • Using the fuzzified coefficients, ARMA processes can be extended to fuzzy time series model. In this paper, the estimation of parameters in the fuzzy time series model with asymmetric triangular fuzzy coefficients is studied. Nonlinear programming is applied to get solutions of parameters.

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A study on the Time Series Prediction Using the Support Vector Machine (보조벡터 머신을 이용한 시계열 예측에 관한 연구)

  • 강환일;정요원;송영기
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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    • pp.315-315
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    • 2000
  • In this paper, we perform the time series prediction using the SVM(Support Vector Machine). We make use of two different loss functions and two different kernel functions; i) Quadratic and $\varepsilon$-insensitive loss function are used; ii) GRBF(Gaussian Radial Basis Function) and ERBF(Exponential Radial Basis Function) are used. Mackey-Glass time series are used for prediction. For both cases, we compare the results by the SVM to those by ANN(Artificial Neural Network) and show the better performance by SVM than that by ANN.

Box-Cox Transformation for Conditional Heteroscedasticity in Domestic Financial Time Series

  • Hwang, S.Y.;Lee, J.H.
    • Journal of the Korean Data and Information Science Society
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    • 제15권2호
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    • pp.413-422
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    • 2004
  • Box-Cox power transformation is employed for analyzing volatilities in Korean financial time series such as KOSPI, KOSDAQ index and interest rates. Statistical procedures for Box-Cox transformed ARCH models are presented. For illustration, diverse financial time series data are analyzed and appropriate power transformations are suggested for each data.

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Model Checking for Time-Series Count Data

  • Lee, Sung-Im
    • Communications for Statistical Applications and Methods
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    • 제12권2호
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    • pp.359-364
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    • 2005
  • This paper considers a specification test of conditional Poisson regression model for time series count data. Although conditional models for count data have received attention and proposed in several ways, few studies focused on checking its adequacy. Motivated by the test of martingale difference assumption, a specification test via Ljung-Box statistic is proposed in the conditional model of the time series count data. In order to illustrate the performance of Ljung- Box test, simulation results will be provided.

Kalman-Filter Estimation and Prediction for a Spatial Time Series Model (공간시계열 모형의 칼만필터 추정과 예측)

  • Lee, Sung-Duck;Han, Eun-Hee;Kim, Duck-Ki
    • Communications for Statistical Applications and Methods
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    • 제18권1호
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    • pp.79-87
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    • 2011
  • A spatial time series model was used for analyzing the method of spatial time series (not the ARIMA model that is popular for analyzing spatial time series) by using chicken pox data which is a highly contagious disease and grid data due to ARIMA not reflecting the spatial processes. Time series model contains a weighting matrix, because that spatial time series model influences the time variation as well as the spatial location. The weighting matrix reflects that the more geographically contiguous region has the higher spatial dependence. It is hypothesized that the weighting matrix gives neighboring areas the same influence in the study of the spatial time series model. Therefore, we try to present the conclusion with a weighting matrix in a way that gives the same weight to existing neighboring areas in the study of the suitability of the STARMA model, spatial time series model and STBL model, in the comparative study of the predictive power for statistical inference, and the results. Furthermore, through the Kalman-Filter method we try to show the superiority of the Kalman-Filter method through a parameter assumption and the processes of prediction.

The Prediction and Analysis of the Power Energy Time Series by Using the Elman Recurrent Neural Network (엘만 순환 신경망을 사용한 전력 에너지 시계열의 예측 및 분석)

  • Lee, Chang-Yong;Kim, Jinho
    • Journal of Korean Society of Industrial and Systems Engineering
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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.

Forecasting Baltic Dry Index by Implementing Time-Series Decomposition and Data Augmentation Techniques (시계열 분해 및 데이터 증강 기법 활용 건화물운임지수 예측)

  • Han, Min Soo;Yu, Song Jin
    • Journal of Korean Society for Quality Management
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    • 제50권4호
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    • pp.701-716
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    • 2022
  • Purpose: This study aims to predict the dry cargo transportation market economy. The subject of this study is the BDI (Baltic Dry Index) time-series, an index representing the dry cargo transport market. Methods: In order to increase the accuracy of the BDI time-series, we have pre-processed the original time-series via time-series decomposition and data augmentation techniques and have used them for ANN learning. The ANN algorithms used are Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) to compare and analyze the case of learning and predicting by applying time-series decomposition and data augmentation techniques. The forecast period aims to make short-term predictions at the time of t+1. The period to be studied is from '22. 01. 07 to '22. 08. 26. Results: Only for the case of the MAPE (Mean Absolute Percentage Error) indicator, all ANN models used in the research has resulted in higher accuracy (1.422% on average) in multivariate prediction. Although it is not a remarkable improvement in prediction accuracy compared to uni-variate prediction results, it can be said that the improvement in ANN prediction performance has been achieved by utilizing time-series decomposition and data augmentation techniques that were significant and targeted throughout this study. Conclusion: Nevertheless, due to the nature of ANN, additional performance improvements can be expected according to the adjustment of the hyper-parameter. Therefore, it is necessary to try various applications of multiple learning algorithms and ANN optimization techniques. Such an approach would help solve problems with a small number of available data, such as the rapidly changing business environment or the current shipping market.

A Neural Network-Driven Decision Tree Classifier Approach to Time Series Identification (인공신경망 기초 의사결정트리 분류기에 의한 시계열모형화에 관한 연구)

  • 오상봉
    • Journal of the Korea Society for Simulation
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    • 제5권1호
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    • pp.1-12
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    • 1996
  • We propose a new approach to classifying a time series data into one of the autoregressive moving-average (ARMA) models. It is bases on two pattern recognition concepts for solving time series identification. The one is an extended sample autocorrelation function (ESACF). The other is a neural network-driven decision tree classifier(NNDTC) in which two pattern recognition techniques are tightly coupled : neural network and decision tree classfier. NNDTc consists of a set of nodes at which neural network-driven decision making is made whether the connecting subtrees should be pruned or not. Therefore, time series identification problem can be stated as solving a set of local decisions at nodes. The decision values of the nodes are provided by neural network functions attached to the corresponding nodes. Experimental results with a set of test data and real time series data show that the proposed approach can efficiently identify the time seires patterns with high precision compared to the previous approaches.

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Kernel-Based Fuzzy Regression Machine For Predicting Turbulent Flows

  • Hong, Dug-Hun;Hwang, Chang-Ha
    • 한국데이터정보과학회:학술대회논문집
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    • 한국데이터정보과학회 2004년도 춘계학술대회
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    • pp.91-101
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    • 2004
  • The turbulent flow is of fundamental interest because the conservation equations for thermodynamics, mass and momentum are linked together. This turbulent flow consists of some coherent time- and space-organized vortical structures. Research has already shown that some dynamic systems and experimental models still cannot provide a good nonlinear analysis of turbulent time series. In the real turbulent flow, very complicated nonlinear behaviors, which are affected by many vague factors are present. In this paper, a kernel-based machine for fuzzy nonlinear regression analysis is proposed to predict the nonlinear time series of turbulent flows. In order to show the practicality and usefulness of this model, we present an example of predicting the near-wall turbulence time series as a verifiable model and compare with fuzzy piecewise regression. The results of practical applications show that the proposed method is appropriate and appears to be useful in nonlinear analysis and in fuzzy environments to predict the turbulence time series.

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