• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1191-1201
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• 2009

The cumulative sum(CUSUM) control charts are typically used for detecting small level shifts in process control. To control an auto-correlated process, the model-based control methods can be employed, in which the residuals from fitting a time series model are applied to the CUSUM chart. However, the persistent level shifts in the original process may lead to varying mean shifts in residuals, which may deteriorate detection performance significantly. Therefore, in this paper, focussing on ARMA(1,1), we propose a new CUSUM type control method which can detect the dynamic mean shifts in residuals especially with oscillating pattern effectively and, through the simulation study, evaluate its performance by comparing with other various CUSUM type control methods introduced so far.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.841-852
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• 2010

An integrated process control (IPC) procedure is a scheme which simultaneously applies the engineering control procedure (EPC) and statistical control procedure (SPC) techniques to reduce the variation of a process. In the IPC procedure, the observed deviations are monitored during the process where adjustments are repeatedly done by its controller. Because the effects of the noise, the special cause, and the adjustment are mixed, the use and properties of the SPC procedure for the out-of-control process are complicated. This paper considers efficiency of EWMA charts for detecting special causes in an ARMA(1,1) noise model with a minimum mean squared error adjustment policy. And we propose the readjustment procedure after having a true signal. This procedure can be considered when the elimination of the special cause is not practically possible.

• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.244-253
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• 2013

In industries, shipping is an important issue in improving the forecasting accuracy of sales. This paper introduces a hybrid method and plural methods are compared. Focusing the equation of exponential smoothing method (ESM) that is equivalent to (1, 1) order autoregressive-moving-average (ARMA) model equation, a new method of estimating the smoothing constant in ESM had been proposed previously by us which satisfies minimum variance of forecasting error. Generally, the smoothing constant is selected arbitrarily. However, this paper utilizes the above stated theoretical solution. Firstly, we make estimation of ARMA model parameter and then estimate the smoothing constant. Thus, theoretical solution is derived in a simple way and it may be utilized in various fields. Furthermore, combining the trend removing method with this method, we aim to improve forecasting accuracy. This method is executed in the following method. Trend removing by the combination of linear and 2nd order nonlinear function and 3rd order nonlinear function is executed to the original production data of two kinds of bread. Genetic algorithm is utilized to search the optimal weight for the weighting parameters of linear and nonlinear function. For comparison, the monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non-monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful for the time series that has various trend characteristics and has rather strong seasonal trend. The effectiveness of this method should be examined in various cases.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.193-203
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• 2016

This paper forecasts electricity demand as a critical element of a demand management system in Smart Grid environment. We present a prediction method of using a combination of predictive values by time series clustering. Periodogram-based normalized clustering, predictive analysis clustering and dynamic time warping (DTW) clustering are proposed for time series clustering methods. Double Seasonal Holt-Winters (DSHW), Trigonometric, Box-Cox transform, ARMA errors, Trend and Seasonal components (TBATS), Fractional ARIMA (FARIMA) are used for demand forecasting based on clustering. Results show that the time series clustering method provides a better performances than the method using total amount of electricity demand in terms of the Mean Absolute Percentage Error (MAPE).

• Journal of Communications and Networks
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• v.14 no.3
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• pp.310-318
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• 2012

Detecting intrusion attacks accurately and rapidly in wireless networks is one of the most challenging security problems. Intrusion attacks of various types can be detected by the change in traffic flow that they induce. Wireless industrial networks based on the wireless networks for industrial automation-process automation (WIA-PA) standard use a superframe to schedule network communications. We propose an intrusion detection system for WIA-PA networks. After modeling and analyzing traffic flow data by time-sequence techniques, we propose a data traffic prediction model based on autoregressive moving average (ARMA) using the time series data. The model can quickly and precisely predict network traffic. We initialized the model with data traffic measurements taken by a 16-channel analyzer. Test results show that our scheme can effectively detect intrusion attacks, improve the overall network performance, and prolong the network lifetime.

• Communications for Statistical Applications and Methods
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• v.20 no.1
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• pp.41-52
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• 2013

The stationary bootstrap of Politis and Romano (1994) is adopted to develop prediction intervals of returns and volatilities in a generalized autoregressive heteroskedastic (GARCH)(p, q) model. The stationary bootstrap method is applied to generate bootstrap observations of squared returns and residuals, through an ARMA representation of the GARCH model. The stationary bootstrap estimators of unknown parameters are defined and used to calculate the stationary bootstrap samples of volatilities. Estimates of future values of returns and volatilities in the GARCH process and the bootstrap prediction intervals are constructed based on the stationary bootstrap; in addition, asymptotic validities are also shown.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.449-453
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• 1987

This paper presents the detecting algorithm of inrush current which causes protective equipment to maloperate in energizing a power distribution line. This detecting algorithm uses the method which extracts the decay component and energy of 2nd harmonics by Prony Spectral Estimation Method existed AR model to new ARMA model. In this paper, the inrush currents are obtained by model simulations and fild tests. By applying these data to the detecting algorithm, it has been confirmed to discriminate inrush currents from fault currents.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.927-936
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• 2017

To analyze longitudinal count data, Poisson linear mixed models are commonly used. In the models the random effects covariance matrix explains both within-subject variation and serial correlation of repeated count outcomes. When the random effects covariance matrix is assumed to be misspecified, the estimates of covariates effects can be biased. Therefore, we propose reasonable and flexible structures of the covariance matrix using autoregressive and moving average Cholesky decomposition (ARMACD). The ARMACD factors the covariance matrix into generalized autoregressive parameters (GARPs), generalized moving average parameters (GMAPs) and innovation variances (IVs). Positive IVs guarantee the positive-definiteness of the covariance matrix. In this paper, we use the ARMACD to model the random effects covariance matrix in Poisson loglinear mixed models. We analyze epileptic seizure data using our proposed model.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.4
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• pp.316-324
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• 2022

This paper uses seasonal auto-regressive integrated moving average (S-ARIMA), which is efficient in seasonality between time-series models, to predict the degradation tendency for lithium-ion batteries and study a method for improving the predictive performance. The proposed method analyzes the degradation tendency and extracted factors through an electrical characteristic experiment of lithium-ion batteries, and verifies whether time-series data are suitable for the S-ARIMA model through several statistical analysis techniques. Finally, prediction of battery aging is performed through S-ARIMA, and performance of the model is verified through error comparison of predictions through mean absolute error.

• Advances in Energy Research
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• v.6 no.2
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• pp.131-143
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• 2019

Global Solar Radiation (GSR) is the key element for performance estimation of any Solar Power Plant (SPP). Its forecasting may help in estimation of power production from a SPP well in advance, and may also render help in optimal use of this power. Seasonal Auto-Regressive Moving Average (SARMA) and Artificial Neural Network (ANN) models are combined in order to develop a hybrid model (SARMA-ANN) conceiving the characteristics of both linear and non-linear prediction models. This developed model has been used for prediction of GSR at Gorakhpur, situated in the northern region of India. The proposed model is beneficial for the univariate forecasting. Along with this model, we have also used Auto-Regressive Moving Average (ARMA), SARMA, ANN based models for 1 - 6 day-ahead forecasting of GSR on hourly basis. It has been found that the proposed model presents least RMSE (Root Mean Square Error) and produces best forecasting results among all the models considered in the present study. As an application, the comparison between the forecasted one and the energy produced by the grid connected PV plant installed on the parking stands of the University shows the superiority of the proposed model.