• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.39-49
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• 2017

As the Internet technology advances, data on the web is increasing sharply. Many research study about incremental learning for classifying effectively in data increasing. Web document contains the time-series data such as published date. If we reflect time-series data to classification, it will be an effective classification. In this study, we analyze the time-series variation of the words. We propose an efficient classification through dividing the dataset based on the analysis of time-series information. For experiment, we corrected 1 million online news articles including time-series information. We divide the dataset and classify the dataset using SVM and $Na{\ddot{i}}ve$ Bayes. In each model, we show that classification performance is increasing. Through this study, we showed that reflecting time-series information can improve the classification performance.

• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.65-73
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• 2001

Until recently, we use only weekly and monthly adjustment factors in order to estimate the AADT. By the way. we can suppose that the traffic is time series data related to flow of time. So we tried to analyse traffic patterns using time series analysis and apply them to estimate the AADT. We could divide traffic patterns into trend, cyclic variation, seasonal variation and irregular variation like as time series data. Also, in order to reduce random error components, we have looked for the weather conditions as an influential factor. There are many weather conditions such as rainfalls, but, temperatures, and sunshine hours among others but we selected rainfalls and lowest temperatures. And then, we have estimated the AADT using time series factors. To compare the results of, we have applied both irregular variation joined to weather factors and that not joined to. RMSE and U-test were opted at methods to appreciate results of AADT estimation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.3
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• pp.361-367
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• 2010

We produced continuous vertical time series of 14 permanent GPS stations operated by National Geographic Information Institute by processing about five years of data. Then we computed the height velocities by using a linear regression fitting of those time series, and did principal component analysis to understand the overall characteristics of the series. The prominent signal obtained as the first mode of PCA results showed an average of 4.2 mm/yr vertical velocity. The values of the first mode eigenvectors were consistent at all sites. Thus, we concluded that all the 14 stations are uplifting nearly at the same velocity for the test period. Then changes of precision before and after removing the first mode signal from the 14 height time series were analyzed. As a result, the precision improved 34.8% on average.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.268-272
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• 2012

현재 확률강우량을 산정할 때는 수문사상 자료계열이 정상성을 가지고 있다고 가정하고 산정하고 있다. 이는 경향성 검정을 통과하지 못한 비정상성을 가지는 자료계열이라 할지라도 이들 자료에 대해 해석을 할 수 있는 검증된 대안이 아직 없기 때문이다. 따라서 본 연구에서는 강우의 증가경향성이 존재하여 경향성 검정을 통과하지 못한 비정상성을 가지는 지역에 대해서 경향성을 고려한 확률강우량을 산정하고, 기존의 방법에 의해서 산정된 확률강우량과 비교해보았다. 그리고 현재까지의 강우량 자료를 시계열분석을 이용하여 미래 강우량 자료를 예측하고 확률강우량을 산정함으로써 시계열분석을 통한 확률강우량 산정과 경향성을 고려하여 산정된 확률강우량을 비교했다. 우선 실제로 우리나라의 강우의 패턴이 변화하고 있는지 확인하고, 변화의 양상이 뚜렷한 지점에 대해서 시계열분석을 이용하여 가까운 미래의 확률강우량을 산정하였다. 그 결과, 2010년에 비해서 2020년의 확률강우량이 4~15%정도 증가하였다. 다른 방법과 비교해본 결과, 약 5%의 편차를 보였다. 본 연구에서는 최종적으로 우리나라 강우관측소 61지점의 경향성을 판별하여 전국 지도에 등고선으로 나타내어 경향성을 고려해야 할 지역들은 분류하였고, 이 지도를 활용하여 확률강우량을 산정함으로써 수공구조물의 계획 및 설계, 하천관리, 수자원 계획 등에 활용하고 전체적인 설계 빈도 상향조정으로 발생되는 예산 낭비 방지와 홍수피해 저감에 도움이 되고자 한다.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.06a
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• pp.28-31
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• 2010

본 연구는 대구시의 상업지역의 용도지역변화를 GIS를 사용하여 시대별로 구축하고 이를 분석함으로써 변화의 특성을 고찰함에 그 목적이 있다. 연구의 방법은 도심기능의 분산과 그에 따른 상업지역을 중심으로 한 용도지역의 변화형태 그리고 상업시설의 입지형태를 분석하였다. 이에 따라 분석한 결과 상업지역의 입지에 따른 주변지역의 용도 지역의 변화와 아울러 가로축의 발달과 함께 상업시설의 형태도 선형으로 발달하며 기능을 분산시키는 것을 알 수 있었다.

• Journal of KIISE:Databases
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• v.33 no.6
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• pp.600-619
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• 2006

According to recent technical advances on sensors and mobile devices, processing of data streams generated by the devices is becoming an important research issue. The data stream of real values obtained at continuous time points is called streaming time-series. Due to the unique features of streaming time-series that are different from those of traditional time-series, similarity matching problem on the streaming time-series should be solved in a new way. In this paper, we propose an efficient algorithm for streaming time- series matching problem that supports normalization transform. While the existing algorithms compare streaming time-series without any transform, the algorithm proposed in the paper compares them after they are normalization-transformed. The normalization transform is useful for finding time-series that have similar fluctuation trends even though they consist of distant element values. The major contributions of this paper are as follows. (1) By using a theorem presented in the context of subsequence matching that supports normalization transform[4], we propose a simple algorithm for solving the problem. (2) For improving search performance, we extend the simple algorithm to use $k\;({\geq}\;1)$ indexes. (3) For a given k, for achieving optimal search performance of the extended algorithm, we present an approximation method for choosing k window sizes to construct k indexes. (4) Based on the notion of continuity[8] on streaming time-series, we further extend our algorithm so that it can simultaneously obtain the search results for $m\;({\geq}\;1)$ time points from present $t_0$ to a time point $(t_0+m-1)$ in the near future by retrieving the index only once. (5) Through a series of experiments, we compare search performances of the algorithms proposed in this paper, and show their performance trends according to k and m values. To the best of our knowledge, since there has been no algorithm that solves the same problem presented in this paper, we compare search performances of our algorithms with the sequential scan algorithm. The experiment result showed that our algorithms outperformed the sequential scan algorithm by up to 13.2 times. The performances of our algorithms should be more improved, as k is increased.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.11a
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• pp.371-374
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• 2003

서브시퀀스 매칭은 주어진 질의 시퀀스와 변화의 추세가 유사한 서브시퀀스들을 시계열 데이터베이스로부터 검색하는 연산이다. 본 논문에서는 기존에 제안된 서브시퀀스 매칭 기법인 FRM과 Dual-Match를 대상으로 다양한 실험을 통하여 윈도우 크기 효과를 정량적으로 분석한다. 또한, 이러한 분석 결과를 기반으로 서브시퀀스 매칭 처리의 성능 개선을 위한 향후의 연구 방향을 제시한다.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.553-568
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• 2022

The Household Income and Expenditure Survey is a representative survey of Statistics Korea, which aims to measure and analyze national income and consumption levels and their changes by understanding the current state of household balances. Recently, the disconnection problem in these time series caused by the large-scale reorganization of the survey methods in 2017 and 2019 has become an issue. In this study, we model the characteristics of the time series in the Household Income and Expenditure Survey up to 2016, and use the modeling to compute forecasts for linking the expenditures in 2017 and 2018. In order to evenly reflect the characteristics across all expenditure item series and to reduce the impact of a specific forecast model, we synthesize a total of 8 models such as regression models, time series models, and machine learning techniques. In particular, the noteworthy aspect of this study is that it improves the forecast by using the optimal combination technique that can exactly reflect the hierarchical structure of the Household Income and Expenditure Survey without loss of information as in the top-down or bottom-up methods. As a result of applying the proposed method to forecast expenditure series from 2017 to 2019, it contributed to the recovery of time series linkage and improved the forecast. In addition, it was confirmed that the hierarchical time series forecasts by the optimal combination method make linkage results closer to the actual survey series.

• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.95-105
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• 2009

Irregular temporal sampling is a common feature of geophysical and biological time series in remote sensing. This study proposes an on-line system for reconstructing observation image series including bad or missing observation that result from mechanical problems or sensing environmental condition. The surface parameters associated with the land are usually dependent on the climate, and many physical processes that are displayed in the image sensed from the land then exhibit temporal variation with seasonal periodicity. An adaptive feedback system proposed in this study reconstructs a sequence of images remotely sensed from the land surface having the physical processes with seasonal periodicity. The harmonic model is used to track seasonal variation through time, and a Gibbs random field (GRF) is used to represent the spatial dependency of digital image processes. In this study, the Normalized Difference Vegetation Index (NDVI) image was computed for one week composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over the Korean peninsula, and the adaptive reconstruction of harmonic model was then applied to the NDVI time series from 1996 to 2000 for tracking changes on the ground vegetation. The results show that the adaptive approach is potentially very effective for continuously monitoring changes on near-real time.

• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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• pp.25-30
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• 2005

경제정책과 관련하여 경제시계열을 작성하는 중요한 목적중 하나는 순환변동을 파악할 수 있는 정보를 제공하는 것이다. 그런데 월별 또는 분기별로 작성되는 경제시계열은 계절변동 및 불규칙변동으로 인해 순환변동 등 기조적 변화를 잘못 파악하기 쉽다. 경제시계열의 기조적 변화를 파악하기 위해서는 원래의 경제시계열에서 계절변동, 불규칙변동을 분해 후 제거해서 분석해야 한다. 이 논문에서는 웨이블렛(wavelet)을 이용하여 시계열을 분해하고 이를 통해 경제시계열의 순환변동 등을 구하고 분해 요소들을 따로 예측한 후 결합된 예측을 시도한다.