• Communications for Statistical Applications and Methods
• /
• v.31 no.1
• /
• pp.143-154
• /
• 2024

This paper considers a combined model of exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models that are commonly used to forecast time series data. The combined model is constructed through an innovational state space model based on the level variable instead of the differenced variable, and the identifiability of the model is investigated. We consider the maximum likelihood estimation for the model parameters and suggest the model selection steps. The forecasting performance of the model is evaluated by two real time series data. We consider the three competing models; ETS, ARIMA and the trigonometric Box-Cox autoregressive and moving average trend seasonal (TBATS) models, and compare and evaluate their root mean squared errors and mean absolute percentage errors for accuracy. The results show that the combined model outperforms the competing models.

• Communications for Statistical Applications and Methods
• /
• v.30 no.3
• /
• pp.273-289
• /
• 2023

In this paper, we develop a new time series model for predicting IPO (initial public offering) data with non-negative integer value. The proposed model is based on integer-valued autoregressive (INAR) model with a Poisson thinning operator. Just as the heterogeneous autoregressive (HAR) model with daily, weekly and monthly averages in a form of cascade, the integer-valued heterogeneous autoregressive (INHAR) model is considered to reflect efficiently the long memory. The parameters of the INHAR model are estimated using the conditional least squares estimate and Yule-Walker estimate. Through simulations, bias and standard error are calculated to compare the performance of the estimates. Effects of model fitting to the Korea's IPO are evaluated using performance measures such as mean square error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) etc. The results show that INHAR model provides better performance than traditional INAR model. The empirical analysis of the Korea's IPO indicates that our proposed model is efficient in forecasting monthly IPO volumes.

• Environmental and Resource Economics Review
• /
• v.17 no.1
• /
• pp.3-22
• /
• 2008

We study nonlinearities of $CO_2$ emissions and economic growth m Korea using the Smooth Transition Autoregressive (or STAR) model. We find evidence for nonlinearities and cyclical regime changes of both time series. In the extended nonlinear empirical work, we characterize dynamic properties of the two time series and then find mutually significant Granger causality between $CO_2$ emissions and economic growth. All these empirical evidences together reinforce long standing concern that economy-wide restrictions on $CO_2$ emissions would hurt economic growth for Korean styled medium industrialized countries.

• Journal of the Korea Society for Simulation
• /
• v.5 no.1
• /
• pp.1-12
• /
• 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.

• Journal of the Korean Statistical Society
• /
• v.36 no.2
• /
• pp.183-200
• /
• 2007

We consider a nonlinear autoregressive moving average model with nonlinear GARCH errors, and find sufficient conditions for the existence of a strictly stationary solution of three related time series equations. We also consider a geometric ergodicity and functional central limit theorem for a nonlinear autoregressive model with nonlinear ARCH errors. The given model includes broad classes of nonlinear models. New results are obtained, and known results are shown to emerge as special cases.

• Journal of Korean Society on Water Environment
• /
• v.31 no.1
• /
• pp.19-28
• /
• 2015

The purpose of this research is reconstruction of annual precipitation based on Tree-ring series at Seorak mountain and examine its effectiveness. To do so we performed nonlinear time series characteristics test of Tree-ring series and reconstructed annual precipitation of Gangneung from 1687 to 1911 using Artificial neural network and Nonlinear autoregressive exogeneous input (NARX) model which reflects stochastic properties. As a result, Tree-ring series at Seorak Mountain shows nonlinear time series property and reconstructed annual precipitation series drawn from NARX is similar in statistical characteristics of observed annual time series.

• Journal of Biomedical Engineering Research
• /
• v.16 no.3
• /
• pp.317-324
• /
• 1995

A need exists for the accurate identification of time series models having time varying parameters, as is important in the case of real time identification of nonstationary EMG signal. Thls paper describes real time identification and muscle fatigue monitoring method of nonstationary EMG signal. The method is composed of the efficient identifier which estimates the autoregressive parameters of nonstationary EMG signal model, and its real time implementation by using T805 parallel processing computer. The method is verified through experiment with real EMG signals which are obtained from surface electrode. As a result, the proposed method provides a new approach for real time Implementation of muscle fatigue monitoring and the execution time is 0.894ms/sample for 1024Hz EMG signal.

• Journal of the Korean Statistical Society
• /
• v.17 no.2
• /
• pp.101-120
• /
• 1988

A time series model with Laplacian (double-exponential) marginal distribution, NLAR(2), was proposed by Dewald and Lewis (1985). The special cases of NLAR(2) process and their properties are considered. Extensions to the NLAR(p) is discussed. It is shown that the NLAR(1) satisfies the strong-mixing conditions, hence the model-free prediction interval using the sample quantiles can be obtained.

• Communications for Statistical Applications and Methods
• /
• v.8 no.2
• /
• pp.565-572
• /
• 2001

This article explores the problem of ergodicity for the nonlinear autoregressive processes with ARCH structure in a very general setting. A sufficient condition for the geometric ergodicity of the model is developed along the lines of Feigin and Tweedie(1985), thereby extending classical results for specific nonlinear time series. The condition suggested is in turn applied to some specific nonlinear time series illustrating that our results extend those in the literature.

• International Journal of Safety
• /
• v.3 no.1
• /
• pp.1-5
• /
• 2004

This study discusses frequency analysis based on autoregressive (AR) time series model, and the characterization of surface quality in orthogonal cutting of a fiber-matrix composite materials. A sparsely distributed idealized composite material, namely a glass reinforced polyester (GFRP) was used as workpiece. Analysis method employs a force sensor and the signals from the sensor are processed using AR time series model. The experimental correlations between the fiber pull-out and AR model coefficients are then established.