• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.141-150
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• 2003

This paper presents the multiple testing method of an autoregressive parameter in stationary AR(1) model using the usual Bayes factor. As prior distributions of parameters in each model, uniform prior and noninformative improper priors are assumed. Posterior probabilities through the usual Bayes factors are used for the model selection. Finally, to check whether these theoretical results are correct, simulated data and real data are analyzed.

• Journal of the Korean Society of Safety
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• v.16 no.1
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• pp.88-93
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• 2001

This study discusses frequency analysis based on autoregressive (AR) time series model, and process characterization 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 resulting pattern vectors of AR coefficients are then passed to the feature extraction block. Inside the feature extraction block, only those features that are most sensitive to different types of cutting mechanisms are selected. The experimental correlations between the different chip formation mechanisms and AR model coefficients are established.

• Journal of Communications and Networks
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• v.13 no.6
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• pp.621-624
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• 2011

With the rapid growth of internet traffic, accurate and reliable prediction of internet traffic has been a key issue in network management and planning. This paper proposes an autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) error model for forecasting internet traffic and evaluates its performance by comparing it with seasonal autoregressive integrated moving average (ARIMA) models in terms of root mean square error (RMSE) criterion. The results indicated that the seasonal AR-GARCH models outperformed the seasonal ARIMA models in terms of forecasting accuracy with respect to the RMSE criterion.

• Geotechnical Engineering
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• v.9 no.3
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• pp.67-76
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• 1993

Korea being composed of a number of mountains has been damaged and destroyed in lives and properties by the occurrence of many landslides during the wet seasons. Therefore, it is necessary to study the forecast system and risk analysis for the occurrence of landslides : the rise of groundwater levels due to rainfall is the main cause of landslides. In this paper, the autoregressive models are used to predict the grondwater levls using cases of both time invariant and time -varing autoregressive coefficients. In the former case, AR(1), AR(2), and AR(3) models are selected and their single-valued parameters are estimated to fit them to the observed groundwater level series. In the latter case, modified AR(1) and typical AR(2) models are used as process model and a discrete Kalman Filtering technique is utilized to estimate the parameters which are themselves a function of time. The results show that the real time forecast system using the time-varying autoregressive coefficinets as well as time -invariant AR model is good to predict the groundwater level in hillside slopes and we might get better result if we use the time-hourly rainfall intensity as well as the observed groundwater level.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.777-786
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• 2001

The autoregressive models have been used to describe a wade variety of time series. Then the problem of determining the order in the times series model is very important in data analysis. We consider the Bayesian approach for finding the order of autoregressive(AR) error models using the latent variable which is motivated by Tanner and Wong(1987). The latent variables are combined with the coefficient parameters and the sequential steps are proposed to set up the prior of the latent variables. Markov chain Monte Carlo method(Gibbs sampler and Metropolis-Hasting algorithm) is used in order to overcome the difficulties of Bayesian computations. Three examples including AR(3) error model are presented to illustrate our proposed methodology.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1017-1030
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• 2016

This study focuses on the system identification of reinforced concrete bridges using vector autoregressive model (VAR). First, the time series output response from a bridge establishes the autoregressive (AR) models. AR models are one of the most accurate methods for stationary time series. Burg's algorithm estimates the autoregressive coefficients (ARCs) at p-lag by reducing the sum of the forward and the backward errors. The computed ARCs are assembled in the state system matrix and the eigen-system realization algorithm (ERA) computes: the eigenvector matrix that contains the vectors of the mode shapes, and the eigenvalue matrix that contains the associated natural frequencies. By taking advantage of the characteristic of the AR model with ERA (ARMERA), civil engineering can address problems related to damage detection. Operational modal analysis using ARMERA is applied to three experiments. One experiment is coupled with an artificial neural network algorithm and it can detect damage locations and extension. The neural network uses a specific number of ARCs as input and multiple submatrix scaling factors of the structural stiffness matrix as output to represent the damage.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1093-1102
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• 2012

In this paper, we predicted the interest spread using the VAR (vector autoregressive) model. Variables used in the VAR model were selected among 56 domestic and foreign macroeconomic time series through crosscorrelation and Granger causality test. The performance of the VAR model was compared with the univariate time series model, AR (autoregressive) model, in view of MAPE (mean absolute percentage error) and RMSE (root mean square error) of forecasts for the last twelve months.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.1
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• pp.81-88
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• 2010

The analysis and synthesis of various types of hydrologic variables such as precipitation, surface runoff, and discharge are usually required in planning and management of water resources. These hydrologic variables are mostly represented using stochastic models. One of which is the autoregressive model, that gives promising results in time series modeling. This study is an application of this model, which aimed to determine the AR model that best represents the historical monthly streamflow of the two gauging stations, namely Andong Dam and Imha Dam, both located in the upper Nakdong River Basin. AR(3) model was found to be the best model for both gauging stations. Parameters of the determined order of AR model ($\phi_1$, $\phi_2$ and $\phi_3$) were also estimated. Using several diagnostic tests, the efficiency of the determined AR(3) model was tested. These tests indicated the accuracy of the determined AR(3) model.

• Wind and Structures
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• v.11 no.3
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• pp.241-256
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• 2008

This paper adopts autoregressive (AR) model to simulate the wind velocity of spatial three-dimensional fields in accordance with the time and space dependent characteristics of the 3-D fields. Based on the built MATLAB programming, this paper discusses in detail the issues of the AR model deduced by matrix form in the simulation and proposes the corresponding solving methods: the over-relaxation iteration to solve the large sparse matrix equations produced by large number of degrees of freedom of structures; the improved Gauss formula to calculate the numerical integral equations which integral functions contain oscillating functions; the mixed congruence and central limit theorem of Lindberg-Levy to generate random numbers. This paper also develops a method of ascertaining the rank of the AR model. The numerical examples show that all those methods are stable and reliable, which can be used to simulate the wind velocity of all large span structures in civil engineering.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.1-17
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• 2013

In this article, several types of hybrid forecasting models are suggested. In particular, hybrid models using the generalized additive model (GAM) are newly suggested as an alternative to those using neural networks (NN). The prediction performances of various hybrid and non-hybrid models are evaluated using simulated time series data. Five different types of seasonal time series data related to an additive or multiplicative trend are generated over different levels of noise, and applied to the forecasting evaluation. For the simulated data with only seasonality, the autoregressive (AR) model and the hybrid AR-AR model performed equivalently very well. On the other hand, if the time series data employed a trend, the SARIMA model and some hybrid SARIMA models equivalently outperformed the others. In the comparison of GAMs and NNs, regarding the seasonal additive trend data, the SARIMA-GAM evenly performed well across the full range of noise variation, whereas the SARIMA-NN showed good performance only when the noise level was trivial.