• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.573-589
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• 2023

In a piecewise linear trend model, the change points coincide with the mean change points of the first differenced time series. Therefore, by detecting the mean change points of the first differenced time series, one can estimate the change points of the piecewise linear trend model. In this paper, based on this fact, a method is proposed for detecting change points of the piecewise linear trend model using the simple moving average of the first differenced time series rather than estimates of the slope or residuals. Our Monte Carlo simulation experiments show that the proposed method performs well in estimating the number of change points not only when the error terms in the piecewise linear trend model are independent but also when they are serially correlated.

• Journal of Korean Society of Transportation
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• v.26 no.1
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• pp.51-63
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• 2008

Jeju-Mainland demand for air passenger is variated by the season because most of the demands stem from the leisure travel. This research is to estimate the econometrics demand models(A simple time series model and the partial adjustment model) and elasticities of each models for the Jeju-Mainland domestic routes air travel market using the time series aggregate data between the year 1996 and 2005. As the result of estimating, income elasticity was evaluated to be elastic(1.55) and fare elasticity was inelastic(-0.49${\sim}$-0.59) for A simple time series models. In the partial adjustment model's case, income elasticity was evaluated to be inelastic(0.51) in short-run whereas it was evaluated to be elastic(1.88) in long-run. Fare elasticity was evaluated to be inelastic in short-run(high-demand season: -0.13, slack season: -0.20) and long-run(high-demand season: -0.48, slack season: -0.72).

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.995-1006
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• 2011

A time series can be decomposed into simple components with a multiscale method. Empirical mode decomposition(EMD) is a recently invented multiscale method in Huang et al. (1998). It is natural to apply a classical prediction method such a vector autoregressive(AR) model to the obtained simple components instead of the original time series; in addition, a prediction procedure combining a classical prediction model to EMD and Hilbert spectrum is proposed in Kim et al. (2008). In this paper, we suggest to adopt principal component analysis(PCA) to the prediction procedure that enables the efficient selection of input variables among obtained components by EMD. We discuss the utility of adopting PCA in the prediction procedure based on EMD and Hilbert spectrum and analyze the daily worm account data by the proposed PCA adopted prediction method.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.715-718
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• 2009

Time series data is a sequence of data points, measured typically at successive, spaced at time intervals. Many devices for an efficient exploration is developed according as the items of time series data increase. Among these devices, there is a Timebox widget as a representative device of dynamic query for interactive data exploration. Timeboxes are rectangular query region of interest. The users can draw the region of interest using simple mouse manipulation and the query result sets is displayed. But there is a limitation to represent the concrete query region and Timeboxes visualize the query region inconsistent with the mental model of users. To resolve these problems, we propose a new device called DQB(Dynamic Query Band). DQB is a qeury region consisting of user defined polyline with a thickness on time series data. This device is possible to concretely specify the query region. Also, it provides a simple and convenient interface and a good conceptual model.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.633-641
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• 2001

The least squares method is usually applied when estimating the parameters in the regression models. However the least square estimator is not very efficient when the distribution of the error is skewed. In this paper, we propose the asymmetric least square estimator for a particular nonlinear time series regression model, and give the simple and practical sufficient conditions for the strong consistency of the estimators.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.721-730
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• 2010

In satellite remote sensing, irregular temporal sampling is a common feature of geophysical and biological process on the earth's surface. Lee (2008) proposed a feed-back system using a harmonic model of single period to adaptively reconstruct observation image series contaminated by noises resulted from mechanical problems or environmental conditions. However, the simple sinusoidal model of single period may not be appropriate for temporal physical processes of land surface. A complex model of multiple periods would be more proper to represent inter-annual and inner-annual variations of surface parameters. This study extended to use a multi-periodic harmonic model, which is expressed as the sum of a series of sine waves, for the adaptive system. For the system assessment, simulation data were generated from a model of negative errors, based on the fact that the observation is mainly suppressed by bad weather. The experimental results of this simulation study show the potentiality of the proposed system for real-time monitoring on the image series observed by imperfect sensing technology from the environment which are frequently influenced by bad weather.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.183-190
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• 2021

In this paper, we implement a system for a fund recommendation based on the investment propensity and for a future fund price prediction. The investment propensity is classified by scoring user responses to series of questions. The proposed system recommends the funds with a suitable risk rating to the investment propensity of the user. The future fund prices are predicted by Prophet model which is one of the machine learning methods for time series data prediction. Prophet model predicts future fund prices by learning the parameters related to trend changes. The prediction by Prophet model is simple and fast because the temporal dependency for predicting the time-series data can be removed. We implement web pages for the fund recommendation and for the future fund price prediction.

• Convergence Security Journal
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• v.11 no.3
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• pp.19-24
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• 2011

Software failure time presented in the literature exhibit either constant monotonic increasing or monotonic decreasing, For data analysis of software reliability model, data scale tools of trend analysis are developed. The methods of trend analysis are arithmetic mean test and Laplace trend test. Trend analysis only offer information of outline content. In this paper, we discuss forecasting failure time case of failure time censoring. In this study, time series analys is used in the simple moving average and weighted moving averages, exponential smoothing method for predict the future failure times, Empirical analysis used interval failure time for the prediction of this model. Model selection using the mean square error was presented for effective comparison.

• Korean Security Journal
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• no.30
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• pp.139-169
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• 2012

The purpose of this study is to contribute to establishing the scientific policing policies through deriving the time series models that can forecast the occurrence of major crimes such as murder, robbery, burglary, rape, violence and identifying the occurrence of major crimes using the models. In order to achieve this purpose, there were performed the statistical methods such as Generation of Time Series Model(C) for identifying the forecasting models of time series, Generation of Time Series Model(C) and Sequential Chart of Time Series(N) for identifying the accuracy of the forecasting models of time series on the monthly incidence of major crimes from 2002 to 2010 using IBM PASW(SPSS) 19.0. The following is the result of the study. First, murder, robbery, rape, theft and violence crime's forecasting models of time series are Simple Season, Winters Multiplicative, ARIMA(0,1,1)(0,1,1), ARIMA(1,1,0 )(0,1,1) and Simple Season. Second, it is possible to forecast the short-term's occurrence of major crimes such as murder, robbery, burglary, rape, violence using the forecasting models of time series. Based on the result of this study, we have to suggest various forecasting models of time series continuously, and have to concern the long-term forecasting models of time series which is based on the quarterly, yearly incidence of major crimes.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.70-73
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• 1996

Many researchers had considered biological systems as linear systems. In many cases of biological systems, the phenomena that show the regular and periodic dynamics are considered the normal state. However, some clinical experiments reported, in some cases, the periodic signals represented the abnormal state. We assume that signals from human body system are generated from deterministic, intrinsic mechanisms and can be represented a simple equation that show nonlinear dynamics dependent on control parameters. The objective of our study is to model a nonlinear dynamics correctly from the nonlinear time series using the genetic programming method; to find a simple equation of nonlinear dynamics using collected time series and its nonlinear characteristics. We applied genetic programming to model RR interval of ECG that shows chaotic phenomena. We used 4 statistic measures and 2 fractal measures to estimate fitness of each chromosome, and could obtain good solutions of which chaotic features are similar.