• Journal of Integrative Natural Science
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• v.7 no.3
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• pp.193-199
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• 2014

As the requirement on the quality of the system has increased, the reliability is very important part in terms of enhance stability and to provide high quality services to customers. Many statistical models have been developed in the past years for the estimation of software reliability. We consider the functions for NHPP software reliability model and time series model in software failure data. We estimate parameters for the proposed models from three data sets. The values of SSE and MSE is presented from three data sets. We compare the predicted number of faults with the actual three data sets using the NHPP software reliability model and time series model.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.467-482
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• 2020

We investigate a monitoring procedure for the early detection of parameter changes in location-scale time series models. We introduce a detector for monitoring procedure based on modified residual cumulative sum (CUSUM). The asymptotic properties of the monitoring procedure are established under the null and alternative hypotheses. Simulation results and data analysis are also provided for illustration.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.2
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• pp.122-139
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• 2008

With the proliferation of in-process measurement technology, autocorrelated data are increasingly common in industrial SPC applications. A number of high performance control charting techniques that take into account the specific characteristics of the autocorrelation through time series modeling have been proposed over the past decade. We present a survey of such methods and analyze and compare their performances for a range of typical autocorrelated process models. One practical concern with these methods is that their performances are often strongly affected by errors in the time series models used to represent the autocorrelation. We also provide some analytical results comparing the robustness of the various methods with respect to time series modeling errors.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.931-954
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• 2003

It is widely recognized that economic time series involved not only the linearities but also the non-linearities. In this paper, when the economic time series data have the nonlinear characteristics we propose the forecasts method using combinations of both forecasts from linear and nonlinear models. In empirical study, we compare the forecasting performance of 4 exchange rates models(AR, GARCH, AR+GARCH, Bilinear model) and combination of these forecasts for dairly Won/Dollar exchange rates returns. The combination method is selected by the estimated individual forecast errors using Monte Carlo simulations. And this study shows that the combined forecasts using unrestricted least squares method is performed substantially better than any other combined forecasts or individual forecasts.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.6
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• pp.918-922
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• 2016

As the number of wind farms operating has increased, the interest of the central unit commitment and dispatch for wind power has increased as well. Wind power forecast is necessary for effective power system management and operation with high wind power penetrations. This paper presents the centralized wind power forecasting method, which is a forecast to combine all wind farms in the area into one, using time series models. Also, this paper proposes a prediction model modified with wind forecast error compensation. To demonstrate the improvement of wind power forecasting accuracy, the proposed method is compared with persistence model and new reference model which are commonly used as reference in wind power forecasting using Jeju Island data. The results of case studies are presented to show the effectiveness of the proposed wind power forecasting method.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.365-376
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• 2003

In this paper we reviewed a variety of non-Gaussian time series models, and studied the model selection criteria such as AIC and BIC to select proper models. We also considered the likelihood ratio test and applied it to analysis of Polio data set.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.63-76
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• 2006

Marine production forecasting in fisheries is a crucial factor for managing and maintaining fishery resources. Thus this paper aims to generate a forecasting model of total marine production. The most generally method of time series model is to generate the most optimal single forecasting model. But the method could induce a different forecasting results when it does not properly infer a model To overcome the defect, I am trying to propose a single forecasting through multiple time series model. In other word, by comparing and integrating the output resulted from ARIMA and VAR model (which are typical method in a forecasting methodology), I tried to draw a forecasting. It is expected to produce more stable and delicate forecasting prospect than a single model. Through this, I generated 3 models on a yearly and monthly data basis and then here I present a forecasting from 2006 to 2010 through comparing and integrating 3 models. In conclusion, marine production is expected to show a decreasing tendency for the coming years.

• Journal of Environmental Science International
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• v.24 no.8
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• pp.1023-1036
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• 2015

A reliable streamflow forecasting is essential for flood disaster prevention, reservoir operation, water supply and water resources management. This study proposes a hybrid model for river stage forecasting and investigates its accuracy. The proposed model is the wavelet packet-based artificial neural network(WPANN). Wavelet packet transform(WPT) module in WPANN model is employed to decompose an input time series into approximation and detail components. The decomposed time series are then used as inputs of artificial neural network(ANN) module in WPANN model. Based on model performance indexes, WPANN models are found to produce better efficiency than ANN model. WPANN-sym10 model yields the best performance among all other models. It is found that WPT improves the accuracy of ANN model. The results obtained from this study indicate that the conjunction of WPT and ANN can improve the efficiency of ANN model and can be a potential tool for forecasting river stage more accurately.

• 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.

• Journal of the Korea Society for Simulation
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• v.5 no.1
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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.