• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.31 no.5
• /
• pp.375-384
• /
• 2013

Most of regions within the city of Mokpo, located on the southwest coast of the Korean Peninsula, are subjected to significant subsidence because about 70% of the city is land reclaimed from the sea (Kim et al., 2005). In this study, we aimed to estimate the rate of subsidence over Mokpo by using PALSAR L-band dataset from 2006 to 2010. Time series analysis was performed as well using GPS surveying data from 2010 to 2012. Results from these two independent datasets are then compared and analyzed over the common period of time. GPS data processing provides the results of seasonal variation on the surface, that is, via repeatedly rising and falling in association with the periodic cycle. Therefore, a time series analysis was performed to calculate the rate of ground subsidence. The deformation rates calculated for the same point are 3.89cm/yr and 2.65cm/yr from the GPS data and SAR data, respectively. SAR and GPS data processing results show a very similar pattern in terms of magnitude of annual subsidence. Thus, if the two datasets are integrated together, new modeling on ground subsidence is feasible. Lastly, subsidence was detected in a landfill area in the city of Mokpo, which has been continuously occurring through 2012.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2020.07a
• /
• pp.249-251
• /
• 2020

본 논문에서는 코로나바이러스감염증19 사태가 국내 취업시장에 어떠한 영향을 미쳤는지에 대해 알아보기 위하여 빅데이터를 활용한 직업 관련 분석 및 시각화를 수행하였다. 빅데이터를 위한 기본 자료는 통계청 자료와 워크넷 Open API를 활용하였으며, 빅데이터 처리 과정을 거쳐 결과값을 예측을 시도하였다. 2020년도 워크넷 Open API를 통해 고용수와 통계청 자료를 통해 비교 분석 및 시각화를 실시하였고, 08년~20년 취업자수를 통해 시계열 분석 및 예측을 진행해 앞으로의 횡보를 예상해보았다. 분석한 결과 19년, 20년도를 비교 분석했을 때에는 크게 차이가 나지 않았다. 추가적으로 시계열 분석기법을 활용해 보았을 때 매년 고용수는 전체적으로 증가하고 4월에는 감소, 7월에는 증가하는 추세가 나왔다. 코로나바이러스감염증19 사태로 인해 공공기관과 언택트 시대에 따른 화상회의나 재택근무로 인해 운수·통신 취업률은 상승한다는 결과값이 도출되었고, 자영업이나 서비스 직업 등은 다른 직종에 비해 큰 감소를 보여줬으나 국가 경제 활성화에 따른 고용수는 점차 증가할 것이라 예측된다.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.31 no.1B
• /
• pp.21-27
• /
• 2011

When execute runoff forecasting, can not remove perfectly uncertainty of forecasting results. But, reduce uncertainty by various techniques analysis. This study applied various forecasting techniques for runoff prediction's accuracy elevation in Gongju basin. statics techniques is ESP, Period Average & Moving average, Exponential Smoothing, Winters, Auto regressive moving average process. Authoritativeness estimation with results of runoff forecasting by each techniques used MAE (Mean Absolute Error), RMSE (Root Mean Squared Error), RRMSE (Relative Root Mean Squared Error), Mean Absolute Percentage Error (MAPE), TIC (Theil Inequality Coefficient). Result that use MAE, RMSE, RRMSE, MAPE, TIC and confirm improvement effect of runoff forecasting, ESP techniques than the others displayed the best result.

• Journal of Korea Society of Industrial Information Systems
• /
• v.26 no.1
• /
• pp.83-95
• /
• 2021

In this study, major research trends at home and abroad were compared and analyzed in order to derive key research fields in the financial security field and to suggest directions. To this end, 689 domestic and 20,736 foreign data were collected from domestic and international academic journal DB, and major research fields related to financial security were extracted through LDA analysis. After that, hot & cold topics were derived through time series linear regression analysis. As a result of the analysis, studies related to government policy issues, personal information, and accredited certification were derived as promising research fields in Korea. In the case of foreign countries, related studies were drawn to develop advanced security systems such as cryptographic protocols and quantum security. Recently, it has become possible to apply various security technologies in Korea through the abolition of public certification. Accordingly, as changes in promising research fields are expected, the results of this study are expected to contribute to the establishment and development of a successful roadmap for domestic financial security.

• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.1771_1772
• /
• 2009

본 연구에서는 안정적인 물 공급과 에너지의 효율적 사용을 위한 단기 물 수요예측에 대하여 데이터 마이닝 기법의 적용성을 검토하고자 한다. 물 공급이 이루어진 요일과 특이일에 대한 시계열 분석을 통한 단기 물 수요예측과 데이터 마이닝 기법을 적용한 결과를 상호 비교하여 데이터 마이닝 기법의 적용성을 제시하고자 한다. 이를 통하여 단기 물 수요예측알고리즘의 실용화 가능성을 높일 뿐만 아니라 실시간 예측을 위한 기초 데이터 마이닝 체계를 구축하고자 한다.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.4
• /
• pp.563-568
• /
• 2007

This paper presents a methodology for predicting nonlinear time series based on the neural network with weighted fuzzy membership functions (NEWFM). The degree of classification intensity is obtained by bounded sum of weighted fuzzy membership functions extracted by NEWFM, then weighted average defuzzification is used for predicting nonlinear time series. The experimental results demonstrate that NEWFM has the classification capability of 92.22% against the target class of GDP. The time series created by NEWFM model has a relatively close approximation to the GDP which is a typical business cycle indicator, and has been proved to be a useful indicator which has the turning point forecasting capability of average 12 months in the peak point and average 6 months in the trough point during 5th to 8th cyclical period. In addition, NEWFM measures the efficiency of the economic indexes by the feature selection and enables the users to forecast with reduced numbers of 7 among 10 leading indexes while improving the classification rate from 90% to 92.22%.

• Journal of the Korean Geophysical Society
• /
• v.2 no.4
• /
• pp.269-276
• /
• 1999

As a part of trying to get the resistivity values correctly from laboratory core resistivity measurement, the effect of sample holders in resistivity measurement was analyzed and a better way to determine the representative resistivity value from the time series resistivity data was searched. Modified GS type and modified two-electrode type sample holders were devised and their effects have been compared with those of GS and two-electrode type sample holders. The modified two-electrode type sample holder has benefits both in repetition and simplicity in data acquisition. The analysis of distribution trend of the time series resistivity data obtained with different kind of sample holders and source frequencies shows that the maximum curvature point method gives the best result in determining representative resistivity value.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.20 no.1
• /
• pp.33-41
• /
• 2014

Unlike the existing regression analysis, this study anticipated future marine traffic volume using time series analysis and artificial neural network model. Especially, it tried to anticipate future marine traffic volume by applying predictive value through time series analysis on artificial neural network model as an additional input variable. This study used monthly observed values of Incheon port from 1996 to 2013. In order for the verification of the forecasting of the model, value for 2013 is anticipated from the built model with observed values from 1996 to 2012 and a proper model is decided by comparing with the actual observed values. Marine traffic volume of Incheon port showed more traffic than average for May and November by 5.9 % and 4.5 % respectably, and January and August showed less traffic than average by 8.6 % and 4.7 % in 2015. Thus, it is found that Incheon port has difference in monthly traffic volume according to the season. This study can be utilized as a basis to reflect the characteristics of traffic according to the season when investigating marine traffic field observation.

• KDI Journal of Economic Policy
• /
• v.14 no.1
• /
• pp.121-145
• /
• 1992

This paper examines characteristics of time series data related to the construction investment(stationarity and time series components such as secular trend, cyclical fluctuation, seasonal variation, and random change) and surveys predictibility, fitness, and explicability of independent variables of various models to build a short-term construction investment forecasting model suitable for current economic circumstances. Unit root test, autocorrelation coefficient and spectral density function analysis show that related time series data do not have unit roots, fluctuate cyclically, and are largely explicated by lagged variables. Moreover it is very important for the short-term construction investment forecasting to grasp time lag relation between construction investment series and leading indicators such as building construction permits and value of construction orders received. In chapter 3, we explicate 7 forecasting models; Univariate time series model (ARIMA and multiplicative linear trend model), multivariate time series model using leading indicators (1st order autoregressive model, vector autoregressive model and error correction model) and multivariate time series model using National Accounts data (simple reduced form model disconnected from simultaneous macroeconomic model and VAR model). These models are examined by 4 statistical tools that are average absolute error, root mean square error, adjusted coefficient of determination, and Durbin-Watson statistic. This analysis proves two facts. First, multivariate models are more suitable than univariate models in the point that forecasting error of multivariate models tend to decrease in contrast to the case of latter. Second, VAR model is superior than any other multivariate models; average absolute prediction error and root mean square error of VAR model are quitely low and adjusted coefficient of determination is higher. This conclusion is reasonable when we consider current construction investment has sustained overheating growth more than secular trend.

• Korean Journal of Construction Engineering and Management
• /
• v.12 no.2
• /
• pp.111-120
• /
• 2011

High-rise buildings have recently increased over the residential, commercial and office facilities, thus an understanding of construction cost for high-rise building projects has been a fundamental issue due to enormous construction cost as well as unpredictable market conditions and fluctuations in the rate of inflation by long-term construction periods of high-rise projects. Especially, recent violent fluctuations of construction material prices add to problems in construction cost forecasting. This research, therefore, develops a time-series model with the Box-Jenkins methodologies and material prices time-series data in Korea in order to forecast future trends of unit prices of required materials. BIM (Building Information Modeling) approaches are also used to analyze injection time of construction resources and to conduct quantity takeoff so that total material price can be forecasted. Comparative analysis of Predictability of tentative ARIMA (Autoregressive Integrated Moving Average) models was conducted to determine optimal time-series model for forecasting future price trends. Proposed BIM based time series forecasting model can help to deal with sudden changes in economic conditions by estimating future material prices.