• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.899-907
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• 2010

Several non-response imputation methods are suggested, however, mainly cross-sectional imputations are studied and applied to this analysis. A simple and common imputation method for panel data is the cross-wave regression imputation or carry-over imputation as a special case of cross-wave regression imputation. This study suggests a multiple imputation method combined time series analysis and cross-sectional multiple imputation method. We compare this method and the cross-wave regression imputation method using MSE, MAE, and Bias. The 2008 monthly labor survey data is used for this study.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.66-80
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• 2012

It is necessary to normalize spectral image values derived from multi-temporal satellite data to a common scale in order to apply remote sensing methods for change detection, disaster mapping, crop monitoring and etc. There are two main approaches: absolute radiometric normalization and relative radiometric normalization. This study focuses on the multi-temporal satellite image processing by the use of relative radiometric normalization. Three scenes of KOMPSAT-2 imagery were processed using the Multivariate Alteration Detection(MAD) method, which has a particular advantage of selecting PIFs(Pseudo Invariant Features) automatically by canonical correlation analysis. The scenes were then applied to detect disaster areas over Sendai, Japan, which was hit by a tsunami on 11 March 2011. The case study showed that the automatic extraction of changed areas after the tsunami using relatively normalized satellite data via the MAD method was done within a high accuracy level. In addition, the relative normalization of multi-temporal satellite imagery produced better results to rapidly map disaster-affected areas with an increased confidence level.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1512-1516
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• 2007

자신의 현재와 과거의 시계열데이터만을 가지고 시계열 모형을 구축하는 단변량 ARIMA모형 분석법과는 달리, 관심의 대상이 되는 출력시계열과 이와 관련있는 입력시계열의 동태적 특성을 나타내는 전이함수모형(Transfer function model)을 사용하여 소양강댐, 충주댐, 화천댐에 대한 월별 수문자료를 이용하여 유입량을 예측해 보고자 한다. 본 연구의 주요 목적은 다변량 추계학적 시스템의 해석을 위한 모형의 추정과 등정을 위한 과정을 개발하는데 있다. 일반적 추계학적 시스템 모형이 표현되며 그것으로부터 수문학적 시스템의 모형을 매우 적절하게 유도하기 위한 다중 입력-단일 출력 TF, TFN모형을 유도하는데 있다. 이 모형은 수문학적 시스템을 위한 경우에 있어 상관된 입력을 설명할 수 있도록 개발된다. 일반적으로 모형을 만드는 전략이 유도되며 실제유역시스템에 적용하여 검토된다. 한강수계 주요 다목적댐인 소양강댐, 충주댐, 화천댐의 수문자료를 가지고 추계학적 모형(TF, TFN)에 의한 결과와 실제유입량을 비교하여 검토하고자 한다.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1379-1391
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• 2020

UAV in the agricultural application are capable of collecting ultra-high resolution image. It is possible to obtain timeliness images for phenological phases of the crop. However, the UAV uses a variety of sensors and multi-temporal images according to the environment. Therefore, it is essential to use normalized image data for time series image application for crop monitoring. This study analyzed the variability of UAV reflectance and vegetation index according to Aviation Image Making Environment to utilize the UAV multispectral image for agricultural monitoring time series. The variability of the reflectance according to environmental factors such as altitude, direction, time, and cloud was very large, ranging from 8% to 11%, but the vegetation index variability was stable, ranging from 1% to 5%. This phenomenon is believed to have various causes such as the characteristics of the UAV multispectral sensor and the normalization of the post-processing program. In order to utilize the time series of unmanned aerial vehicles, it is recommended to use the same ratio function as the vegetation index, and it is recommended to minimize the variability of time series images by setting the same time, altitude and direction as possible.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.147-149
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• 2020

코로나바이러스의 세계 확산, 발병 이후 사람들의 실내활동 증가와 건강, 면역에 대한 관심은 많이 증가했다. 이에 맞춰 더욱 정교하고 바른 정보에 의한 스마트헬스케어 역시 관심이 증대되고 있다. 여기서 이야기하는 스마트헬스케어의 범위는 영상 장치를 비롯해 다양한 센서를 활용해 신체활동을 모니터링하고 분석하며 기존의 방식보다 더 객관적인 정보를 제공해 주는 것을 말한다. 위 기술과 대중의 관심을 바탕으로 하여 본 연구에서는 다중 모드 센서를 신체에 부착하여 신체활동을 모니터링 하는 시스템 개발을 목적으로 한다. 하드웨어 설계 부분에서 설계가 완성된 Arduino nano 33 Sense를 이용하여 스마트 헬스 실험 시간을 대폭 줄였다. 또한 운동과 같은 시계열 데이터를 분석하기 좋은 LSTM 기법을 채택하였으며, 개발된 모델을 추후 활용할 방안에 대해 논하였다.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.139-148
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• 2019

This study proposed a multiple intervention time series model to predict KTX passenger demand. In order to revise the research of Kim and Kim (Korean Society for Railway, 14, 470-476, 2011) considering only the intervention of the second phase of Gyeong-bu before November of 2011, we adopted multiple intervention seasonal ARIMA models to model the time series data with additional interventions which occurred after November of 2011. Through the data analysis, it was confirmed that the effects of various interventions such as Gyeong-bu and Ho-nam 2 phase, outbreak of MERS and national holidays, which affected the KTX transportation demand, are successfully explained and the prediction accuracy could be quite improved significantly.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.589-599
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• 2018

The land cover map has been produced by using satellite and aerial images. However, these two images have the limitations in spatial resolution, and it is difficult to acquire images of a area at desired time because of the influence of clouds. In addition, it is costly and time-consuming that mapping land cover map of a small area used by satellite and aerial images. This study used multispectral camera-based drone to acquire multi-temporal images for orthoimages generation. The efficiency of produced land cover map was evaluated using time series analysis. The results indicated that the proposed method can generated RGB orthoimage and multispectral orthoimage with RMSE (Root Mean Square Error) of ${\pm}10mm$, ${\pm}11mm$, ${\pm}26mm$ and ${\pm}28mm$, ${\pm}27mm$, ${\pm}47mm$ on X, Y, H respectively. The accuracy of the pixel-based and object-based land cover map was analyzed and the results showed that the accuracy and Kappa coefficient of object-based classification were higher than that of pixel-based classification, which were 93.75%, 92.42% on July, 92.50%, 91.20% on October, 92.92%, 91.77% on February, respectively. Moreover, the proposed method can accurately capture the quantitative area change of the object. In summary, the suggest study demonstrated the possibility and efficiency of using multispectral camera-based drone in production of land cover map.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.347-356
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• 2022

This article is concerned with asymmetric volatility models for financial time series. A generalization of standard single-threshold volatility model is discussed via multiple-threshold in which we specialize to twothreshold case for ease of presentation. An empirical illustration is made by analyzing S&P500 data from NYSE (New York Stock Exchange). For comparison measures between competing models, parametric bootstrap method is used to generate forecast distributions from which summary statistics of CP (Coverage Probability) and PE (Prediction Error) are obtained. It is demonstrated that our suggestion is useful in the field of asymmetric volatility analysis.

• Journal of the Korean earth science society
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• v.32 no.3
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• pp.249-264
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• 2011

A multi-Gaussian kriging approach extended to space-time domain is presented for uncertainty modeling as well as time-series mapping of environmental variables. Within a multi-Gaussian framework, normal score transformed environmental variables are first decomposed into deterministic trend and stochastic residual components. After local temporal trend models are constructed, the parameters of the models are estimated and interpolated in space. Space-time correlation structures of stationary residual components are quantified using a product-sum space-time variogram model. The ccdf is modeled at all grid locations using this space-time variogram model and space-time kriging. Finally, e-type estimates and conditional variances are computed from the ccdf models for spatial mapping and uncertainty analysis, respectively. The proposed approach is illustrated through a case of time-series Particulate Matter 10 ($PM_{10}$) concentration mapping in Incheon Metropolitan city using monthly $PM_{10}$ concentrations at 13 stations for 3 years. It is shown that the proposed approach would generate reliable time-series $PM_{10}$ concentration maps with less mean bias and better prediction capability, compared to conventional spatial-only ordinary kriging. It is also demonstrated that the conditional variances and the probability exceeding a certain thresholding value would be useful information sources for interpretation.

• Journal of Internet Computing and Services
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• v.6 no.1
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• pp.105-114
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• 2005

This paper describes a new measuring technique by analysing multiple time-series patterns. This paper's goal is that extracts a really measured value having a sample pattern which is the best matched with an inputted time-series, and calculates a difference ratio with the value. Therefore, the proposed technique is not a recognition but a measurement. and not a hardware but a software. The proposed technique is consisted of three stages, initialization, learning and measurement. In the initialization stage, it decides weights of all parameters using importance given by an operator. In the learning stage, it classifies sample patterns using LBG and DTW algorithm, and then creates code sequences for all the patterns. In the measurement stage, it creates a code sequence for an inputted time-series pattern, finds samples having the same code sequence by hashing, and then selects the best matched sample. Finally it outputs the really measured value with the sample and the difference ratio. For the purpose of performance evaluation, we tested on multiple time-series patterns obtained from etching machine which is a semiconductor manufacturing.