• Journal of Positioning, Navigation, and Timing
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• v.4 no.3
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• pp.131-140
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• 2015

Antennas at permanent GPS stations operated by the former Ministry of Government Administration and Home Affairs (MOGAHA) in Korea were replaced in years 2008 and 2009, and these changes caused abrupt discontinuities in precise coordinate time series. In this study, an algorithm that eliminates those breaks was developed based on 15-year-long coordinate time series for the purpose of creating clean and continuous coordinate time series. The newly developed algorithm to correct for sudden jumps and dips in the GPS time series due to the antenna change was designed to consider all the linear and annual signals observed before and after the event. The accuracy of the new algorithm was confirmed to be at the Root Mean Square Error (RMSE) level of 2.3-2.6 mm. The new algorithm was also found to be capable of reflect site-specific characteristics at each station.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.329-332
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• 2007

Annual vegetation growth patterns are determined by the intrinsic phenological characteristics of each land cover types. So, if typical growth patterns of each land cover types are well-estimated, and a NDVI time-series data of a certain area is compared to those estimated patterns, we can implement more advanced analyses such as a land surface-type classification or a land surface type change detection. In this study, we utilized Terra MODIS NDVI 250m data and compressed full annual NDVI time series data into several indices using the Harmonic Analysis of Time Series(HANTS) algorithm which extracts the most significant frequencies expected to be presented in the original NDVI time-series data. Then, we found these frequencies patterns, described by amplitude and phase data, were significantly different from each other according to vegetation types and these could be used for land cover classification. However, in spite of the capabilities of the HANTS algorithm for detecting and interpolating cloud-contaminated NDVI values, some distorted NDVI pixels of June, July and August, as well as the long rainy season in Korea, are not properly corrected. In particular, in the case of two or three successive NDVI time-series data, which are severely affected by clouds, the HANTS algorithm outputted wrong results.

• Communications for Statistical Applications and Methods
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• v.21 no.6
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• pp.471-486
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• 2014

This study introduces a new type of symbolic data, a candle chart-valued time series. We aggregate four stock indices (i.e., open, close, highest and lowest) as a one data point to summarize a huge amount of data. In other words, we consider a candle chart, which is constructed by open, close, highest and lowest stock indices, as a type of symbolic data for a long period. The proposed candle chart-valued time series effectively summarize and visualize a huge data set of stock indices to easily understand a change in stock indices. We also propose novel approaches for the candle chart-valued time series modeling based on a combination of two midpoints and two half ranges between the highest and the lowest indices, and between the open and the close indices. Furthermore, we propose three types of sum of square for estimation of the candle chart valued-time series model. The proposed methods take into account of information from not only ordinary data, but also from interval of object, and thus can effectively perform for time series modeling (e.g., forecasting future stock index). To evaluate the proposed methods, we describe real data analysis consisting of the stock market indices of five major Asian countries'. We can see thorough the results that the proposed approaches outperform for forecasting future stock indices compared with classical data analysis.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.1308-1314
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• 2008

In this paper, Seoulmetro that is the first operation organization which operates a city railroad rolling-stock maintenance RIMS(rolling stock information maintenance system) collected and analyzed a light maintenance data and introduced time a series analysis technique to find the way how to contribute to a use efficiency improvement of a city railroad. The purpose of time a series analysis is to remove a seasonal change including data and to check an irregular fluctuation. First of all, a collection range of the data comes under a light maintenance, however it needs a data of more than 3 years to check the seasonal change. We put a study for an accumulated scope that the data satisfy a period like this and are able to extend a range of the study when time flys forward. The data used for study is filtered using a movement average method after passing proper selection working and is solved with a method which looks for season index. Using the season index that was getten in here, we predict a light working frequency, if it has an irregular change, we will contribute it to a city railroad a use efficiency improvement and establish the cause by carrying out prevent maintenance in advance.

• 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.

• Proceedings of the KSRS Conference
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• v.2
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• pp.764-767
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• 2006

It can be important framework data to monitor the change of land-use pattern of river boundary in design and management of river. This study analyzed the change of land-use pattern of Gab- and Yudeung River using time-series aerial images. To do this, we carried out radiation and geometric correction of image, and estimated land-use changes in inland and floodplain. As the analysis of inland, the ratio of residential, commercial, industrial, educational and public area, that is urbanized element, increases, but that of agricultural area shows a decline on the basis of 1990. Also, Minimum Distance Method, which is a kind of supervised classification method, is applied to extract water-body and sand bar layer in floodplain. As the analysis of land-use, the ratio of level-upped riverside land and water-body increases, but that of sand bar decreases. These time-series land use information can be important decision making data to evaluate the urbanization of river boundary, and especially it gives us goodness in river development project such as the composition of ecological habitat.

• East Asian mathematical journal
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• v.35 no.3
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• pp.305-318
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• 2019

Over the past decades, the Lee-Carter model [1] has attracted much attention from various demography-related fields in order to project the future mortality rates. In the Lee-Carter model, the speed of mortality improvement is stochastically modeled by the so-called mortality index and is used to forecast the future mortality rates based on the time series analysis. However, the modeling is applied to long time series and thus an important structural change might exist, leading to potentially large long-term forecasting errors. Therefore, in this paper, we are interested in detecting the structural change of the Lee-Carter model and investigating the actuarial implications. For the purpose, we employ the tests proposed by Coelho and Nunes [2] and analyze the mortality data for six countries including Korea since 1970. Also, we calculate life expectancies and whole life insurance premiums by taking into account the structural change found in the Korean male mortality rates. Our empirical result shows that more caution needs to be paid to the Lee-Carter modeling and its actuarial applications.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.2136-2140
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• 2008

Recently environmental and ecological river management have been held in high repute, therefore it needs to river restoration and management works considering topographical shape of river. This study estimated the change of topographical shape in Gab- and Yudeung-River using time-series aerial photos. Especially, we selected location points of river facilities as weir and bridge that were built and removed and the confluence of Gab- and Yudeung-River. And we investigated the change of time-series flux and flow-direction. Also, through the estimation of sediment by river flow together, it is possible to supply decision making data that is very important to instream flow and environmental and ecological river restoration in urban stream.

• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.587-594
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• 2009

In this paper the change point problem of the error terms in linear regression models is considered. Since fixed or stochastic independent variables and weakly dependent errors are assumed, usual multiple regression models and time series models including ARMA are covered. We use the estimates of probability density function based on residuals in order to test the distribution change of the unobserved errors. Under some mild conditions, the test using the residuals is proved to have the same limiting distribution as the test based on true errors.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.807-819
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• 2013

A recurrence plot can be used as a graphical exploratory data analysis tool before confirmatory time series analysis. With the recurrence plot, we can obtain the structural pattern of the time series and recognize the structural change points in a time series at a glance. Korean stock data shows the usefulness of the recurrence plot as a graphical exploratory data analysis tool for time series data.