• Journal of Korean Institute of Industrial Engineers
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• v.25 no.1
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• pp.111-124
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• 1999

This evapaper is toluate the forecasting performance of three neural network(NN) approaches against ARIMA model using the famous time series analysis competition data. The first NN approach is to analyze the second Makridakis (M2) Competition Data using Multilayer Perceptron (MLP) that has been the most popular NN model in time series analysis. Since it is recently known that MLP suffers from bias/variance dilemma, two approaches are suggested in this study. The second approach adopts Cascade Correlation Network (CCN) that was suggested by Fahlman & Lebiere as an alternative to MLP. In the third approach, a time series is separated into two series using Noise Filtering Network (NFN) that utilizes autoassociative memory function of neural network. The forecasts in the decomposition analysis are the sum of two prediction values obtained from modeling each decomposed series, respectively. Among the three NN approaches, Decomposition Analysis shows the best forecasting performance on the M2 Competition Data, and is expected to be a promising tool in analyzing socio-economic time series data because it reduces the effect of noise or outliers that is an impediment to modeling the time series generating process.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.64-71
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• 2002

This research developed construction cost forecasting model using Building Construction Cost Index, time series analysis and Artificial Neural Networks. By this model, we could calculate the forecasted values of construction cost precisely and efficiently. And we also could find out that the standard deviation of forecasted values is 0.375 and it is a very exact result, so the standard deviation is just 0.33 percent of 112.28, the average of Building Construction Cost Index. And it show more exact forecasting result in comparison with Time Series Analysis.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1103-1112
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• 2013

Exploratory data analysis focuses mostly on data exploration instead of model fitting. We can use the recurrence plot as a graphical exploratory data analysis tool. With the recurrence plot, we can obtain the structural pattern of the time series and recognize the structural change points in time series at a glance.

• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.5-14
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• 2017

Korea, which has undergone a rapid urbanization, faces various problems such as the management of facilities, safety, environment and transportation. To solve civil complaints, local governments receive electronic complaints, but complaints are increasing. Therefore, this study conducted the spatial distribution pattern analysis and the trend analysis by presenting location data on spatial information through Geo-coding by collecting electronic civil petition data over the last 10 years targeting Jinju city. Using the ARIMA model, this study predicted the occurrence of complaints over the next two years (2016~2017) through a time series forecast analysis. As a result, the complaints related to illegal parking were the highest, the complaint related to noise was the second highest, and the complaints related to illegal garbage dumping was the third highest. In addition, the analysis of the spatial distribution pattern shows that the largest hot spot was formed in the central commercial district every year. As a result of the time series forecasting analysis for the crackdown of the illegal parking, complaints increased slightly. To compare the predicted value and the actual data showed a similar pattern. It is judged that this study will be utilized to establish effective countermeasures against civil complaints.

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

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.579-599
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• 2014

The purpose of this study was to identify the trends and risk of bias of research using time series analysis on health and welfare in Korea and to suggest a direction for future health and welfare research. The database searches identified 6,543 papers. Following the process for screening and selecting, a total of 91 papers were included in the systematic review. There has been a steady increase in the number of articles using time series analysis from 1987 to 2013. Time series analysis was applied in medicine and health science journals. The main goals were explanation and description. Most of the subjects were heath status and utilization of healthcare services. The main model used in the time series analysis was ARIMA followed by time series regression. The data were gathered from various sources, including the national statistical office and government agencies. For assessing risk of bias, some studies were found to have inadequate sample sizes or showed no time series graphs and plots. These findings suggest greater widespread utilization of time series analysis in the field of health and welfare and to use the appropriate analysis methods and statistical procedures to obtain more reliable results to improve the quality of research.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.50-53
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• 2017

Not only growth of importance to understanding economic trends, but also the prediction to overcome the uncertainty is coming up for long-term maritime recession. This paper discussed about the prediction of BDI with artificial neural networks (ANN). ANN is one of emerging applications that can be the finest solution to the knotty problems that may not easy to achieve by humankind. Proposed a prediction by implementing neural networks that have recurrent architecture which are a Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). And for the reason of comparison, trained Multi Layer Perceptron (MLP) from 2009.04.01 to 2017.07.31. Also made a comparison with conventional statistics, prediction tools; ARIMA. As a result, recurrent net, especially RNN outperformed and also could discover the applicability of LSTM to specific time-series (BDI).

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.352-352
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• 2011

최근 이상기후 및 집중호우 등의 영향으로 국지적으로 큰 강우가 발생하여 재산피해 및 인명피해를 발생시키고 있으며, 과거 강우발생 빈도에 비하여 큰 강우가 발생되고 있다. 이러한 증가되는 추세에 대하여 확률강우량 산정시 반영하고 있는 추세이며(한만신, 2005), 이렇게 반영된 결과는 확률강우량의 증가와 함께 설계시 반영되어 안전하게 수공구조물을 시공하게 된다. 하지만, 이러한 강우의 경향을 단순하게 증가추세로만 판단하여 미래의 강우를 증가라는 개념으로 검증 절차없이 도입하기에는 과대 추정될 우려가 있으며, 과대 추정된 확률강우량은 결국 시공비의 증가를 유도하여 경제적으로 불이익이 발생한다. 따라서, 과거의 강우자료를 통하여 분석된 최근의 강우 예측결과가 어느정도의 타당성을 갖고 설계된 것인지 판단하여 향후 강우 예측을 통한 확률강우량 산정시 반영하여야 할 것으로 판단된다. 본 연구에는 강우 예측을 위하여 사용되고 있는 ARIMA 모형을 이용하여 인천지역의 1961년~2005년까지의 강우자료를 이용하여 2010년까지의 강우를 예측함으로써 실제 강우자료와의 비교를 통하여 강우 예측의 신뢰성을 검토하여 미래 강우에 대한 예측에 있어서 보다 신뢰성을 확보하고자 하였다. 또한, 강우추세에 의한 인천지역의 확률강우량을 산정함으로써 동일 유역에서의 다른 분포형이나 확률강우를 사용함으로써 발생되는 설계의 혼란을 방지하고자 한다. 본 연구를 위하여 인천지방 기상대의 관측자료를 이용하여 1961년부터 2010년까지의 분단위 강우자료를 획득하였으며, 임의시간에 의한 지속시간별 최대강우량을 산정함으로써 기존의 설계에서 사용되어 왔던 고정시간의 환산계수 대신 실제 최대강우량을 이용함으로써 강우 예측에 대한 정확도를 향상하였고, 확률강우강도식 선정시 지역 강우 특성을 고려하여 결정하여야 한다는 결론을 도출하였다.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.193-203
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• 2016

This paper forecasts electricity demand as a critical element of a demand management system in Smart Grid environment. We present a prediction method of using a combination of predictive values by time series clustering. Periodogram-based normalized clustering, predictive analysis clustering and dynamic time warping (DTW) clustering are proposed for time series clustering methods. Double Seasonal Holt-Winters (DSHW), Trigonometric, Box-Cox transform, ARMA errors, Trend and Seasonal components (TBATS), Fractional ARIMA (FARIMA) are used for demand forecasting based on clustering. Results show that the time series clustering method provides a better performances than the method using total amount of electricity demand in terms of the Mean Absolute Percentage Error (MAPE).

• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.485-500
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• 1999

The systematic forecast of interest rates with liberalization was on the rise to important problems in the money market. Liberalization and globalization of the money market produced a seriously change as a compatition among the money market. Profits of an organ of monetary circulation are, also, definitively influenced by a change of interest rates. Hence most of the organ of monetary circulation studied to a scientific and systematic analysis for deterministic factors which have an effect on interest rates and progress development of a forecasting model of interest rates. In this paper, we develope the forecasting system which has highly forecasting performance based on a number of time series models for interest rates and discuss practical use of this system.